phd quant jobs

How To Get A Quant Job Once You Have A PhD

In this article we are going to discuss an issue that repeatedly crops up via the QuantStart mailbox, namely how to get a quant job once you have a PhD . There's a lot of confusion around this topic because quite a few people who currently work in academia and want to make the shift believe that it is quite straightforward to "walk into" a high-paying financial role. While this may have been true 10-15 years ago, the reality of the current job market is such that quant roles are now highly competitive and candidates need to stand out if they are to get the best jobs.

Firstly we'll discuss what sort of candidates you will be competing against when considering going for interview. Secondly, we'll discuss how to make an honest assessment of your PhD and what you got out of it that might be relevant to quantitative finance roles. Finally, we'll consider whether it is necessary to return to school in order to train up in a quant-specific qualification.

The Competition

I've made it rather clear on QuantStart that the competition for some of the top quantitative trading researcher roles can be extremely tough. In the UK the best roles tend to be filled well upstream of any "front door" interview process. Usually extremely bright academics in mathematics, physics, computer science, economics or mathematical finance are head-hunted for a particular skill set, such as deep expertise on market microstructure, insight into high-frequency trading algorithms, novel stochastic calculus techniques for certain derivatives pricing regimes or extensive statistical machine learning knowledge that applies to datasets used by such funds.

When such quant researcher roles ARE opened up to the public they will often state that they are looking for "only the best and brightest", which in the UK usually means "Top Five" universities (Cambridge, Oxford, Imperial College, LSE and UCL). In the US this will mean high-end Ivy League institutions. The adverts will often state that they want to see evidence of consistent Mathematical Olympiad prizes and an extensive publication list in a relevant field.

While this is certainly true of the top roles, there are plenty of other (very well paying and prestigious) jobs that also need filling. Bear in mind that there are only so many Mathematical Olympiad winners, after all! Thus one should not be disheartened when seeing numerous adverts asking for such qualifications. There are plenty of smaller funds and boutique outfits that do not have the resources to aggressively hunt for the ultimate talent and so will be more than willing to employ bright PhDs who might not necessarily have an Olympiad track record.

Honestly Assess Your PhD

The first task to carry out when applying for quant roles is an honest assessment of your PhD and what you achieved with it . Primarily you need to consider the level of mathematical ability you were able to attain as well as your computational programming skill.

Quant roles in the derivative pricing space, known traditionally as the "quant analyst" or "financial engineer", require a reasonable amount of mathematical sophistication. Specifically, expertise in stochastic calculus, probability and measure theory. These are topics usually taught in an undergraduate mathematics course, but can form a component of taught graduate school PhDs. In addition they require a good understanding of scientific programming usually in C++, Python or MatLab. Since the role of a quant analyst is often to code up an implementation of a particular algorithm from a research paper, under heavy deadlines, it is quite naturally suited to those with PhDs of this type.

Quant roles in the algorithmic trading and quant hedge fund world are almost exclusively going to require novel methods for generating "alpha" (i.e. excess return above a benchmark). Usually this is accomplished via time series analysis and econometrics, but more recently statistical machine learning techniques have been applied, as have methods related to sentiment analysis. Some of the best quant funds make extensive use of even more advanced graduate level mathematics in the realms of algebraic geometry, number theory and information theory. Hence anything highly mathematically, statistically or physically oriented is likely to be of interest to a top quant hedge fund.

As for computer scientists and strong scientific software developers, generally there is always work available for quantitative developer roles. Although you will be competing against those with industry experience in rigourous software engineering. Hence "academic code" of the "20,000 line single-file of Fortran" variety might be a bit of a hindrance! Make sure to brush up on the more modern software development methodologies such as OOP , Agile , etc.

I want to discuss specific PhD fields as well, to give you an idea of where you might consider focusing your efforts based on what you have previously studied:

• Pure Mathematics - The top funds generally hire the pure mathematicians from esoteric realms such as algebraic geometry and information theory. Banks will also take individuals who study stochastic calculus to a high level for their derivatives research teams.
• Mathematical Finance - Portfolio optimisation and derivatives pricing are two common themes studied in mathematical finance PhDs. You will often have collaborated with banks during your PhD, so it is unlikely your job prospects will be slim! If you are struggling, it can be very helpful to contact department heads as they will often have a strong network.
• Theoretical Physics - Funds will be very interested in your ability to model physical phenomena, either through direct or statistical approaches. Some theoretical physics areas are highly mathematical (Cosmology, String Theory, Quantum Field Theory etc) and so the advice given to theoretical physics PhDs is similar to pure mathematicians.
• Computational Physics/Engineering - The main skillset taught here is how to take an algorithm and produce a robust scientific computing implementation, perhaps in a parallelised fashion. This is an extremely useful skill for quant work both in banks and funds, especially for developing infrastructure. Make sure however to brush up on core topics such as statistics and stochastic calculus prior to interview.
• Statistics/Econometrics - Statisticians and theoretical econometricians will be in good demand from technical quant funds, especially in the Commodity Trading Advisor (CTA)/Managed Futures space. The time series modelling will be highly appropriate here.
• Computer Science/Machine Learning - Many funds are now making extensive use of machine learning and optimisation tools, which are the natural domain of the theoretical computer scientist and, more recently, the "data scientist". Familiarity with statistical machine learning and Bayesian methods will be highly attractive.
• Bioinformatics - Bioinformaticians also make extensive use of machine learning tools on "big data" sets. For interview you will want to emphasise your familiarity with such tools and your programming capability. Depending upon your background you may need to brush up on your (pure) mathematics for interview questions.
• Economics/Finance - Economics and Finance PhDs do not always teach you the mathematical maturity necessary for pure quant work, but it really depends on the project. You will need to be honest with yourself about where you lie on the mathematical spectrum. In addition you will need to consider your programming ability.

Heading Back To School

An extremely common question that I receive in the QuantStart mailbag is whether to return to school for finance-specific training subsequent to a PhD.

I've previously documented my views on Masters in Financial Engineering (MFE) programs as related to quantitative trading . In essence I believe that MFEs are not hugely suitable for quantitative trading research work, but they are a good entry point into investment banking quant work.

If your PhD was not heavy on quantitative or programming work, but you have a sufficiently mature mathematical background, then it can make good sense to take a MFE assuming that you can afford to fund the course. A MFE at a top-tier school will provide you with a solid network of other candidates (and thus people who might later help you secure a role), a relatively healthy recruitment position upon graduation and a useful skillset for investment banking derivatives pricing work.

I would advise against returning to school if you have a strong quantitative PhD as you simply won't need the additional qualifications and you should be able to pick up the necessary interview material yourself, albeit with a lot of study.

If you have a PhD in a non-quantitative field and your background is not sufficiently mathematical, then you should definitely consider that you will likely need to return to school if you truly want to work in quantitative finance. In particular you will need to study an undergraduate degree that has a strong quantitative component such as Mathematics or Physics as these two degrees will generally let you transition into other quantitative fields easily.

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Quantitative Analytics Program

Explore our quantitative analytics program.

The rotational Quantitative Analytics program is designed to provide you with the opportunity to gain comprehensive professional and industry experience that prepares you to develop, implement, calibrate, and validate various analytical models. Wells Fargo hires a number of PhDs and Master’s Candidates within the Capital Markets, and Risk Analytics and Decision Science teams.

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What will you do?

Your responsibilities include, but are not limited to:

Developing and validating models for different uses under the direction of experienced team members according to the track of your choice: 

The Capital Markets Track  deals with the mathematical models for pricing, hedging and risking complex financial instruments. Wells Fargo trading portfolios include products in all traded asset classes such as credit, commodity, Equity, FX Rate, Mortgages, and Asset-Backed Finance.

The Risk Analytics & Decision Science Track  deals with the statistical, econometric, and machine-learning/AI models for a variety of applications, including loss and revenue forecasting, credit decisions, financial crimes, fair lending, operational risks, and analysis of unstructured data such as text and audio.

• Use Python, R, C++, SAS, SQL or other programming languages as well as mathematical/statistical packages for model development and validation
• Perform mathematical model development and validation (risk assessment) under the direction of experienced team members
• Produce required documentation to evidence model development or validation
• Understand business needs and providing possible solutions through clear verbal and written communications to management and fellow team members
• Stay up to speed on industry challenges and new and innovative modeling techniques used across Wells Fargo to solve business problems or enhance business capabilities.
• Participate in model related projects for varying purposes, methodologies and relevant lines of business

Is this opportunity right for you?

Program structure and desired qualifications:

• Full-time program for Master's and PhD candidates. This is a 12-month rotational program that starts in July (1-month classroom training followed by two rotations).
• Summer internship for Master's and PhD candidates. Program length is 10 weeks.
• Enrolled in a Master’s or PhD program in: Statistics, Applied or Computational Mathematics, Computer Science, Economics, Physics, Quantitative Finance, Operations Research, Data Science, Engineering or related quantitative field or a related quantitative field
• Excellent computer programing skills and use of statistical software packages such as Python, R, SAS, SQL, Spark, Java, and C++
• Strong verbal, written communication and interpersonal skills
• For the Capital Markets Track : Experience and demonstrated knowledge in mathematical and numerical methods including Monte Carlo methods, differential equations, linear algebra, applied probability, and statistics
• For the Risk Analytics & Decision Science Track : Experience and demonstrated first-hand knowledge in a number of these areas: data analysis, statistical modeling, machine learning/AI models, data management, and computing

What does my future look like?

Upon successful completion of the program, participants will be permanently placed in one of Wells Fargo's model development or model validation groups:

• Artificial Intelligence Machine Learning Model Development
• Traded Products Model Development
• Risk Modeling Group
• Market and Counterparty Risk Analytics
• Mortgage Model Development
• Corporate Model Risk
• Commercial Banking Model Development
• Consumer Modeling

Where are the opportunities?

Summer internship and full-time opportunities are located in Charlotte, NC. Additional locations may be added based on business needs.

Helpful resources

Learn about the Centers of Excellence of the Quantitative Analytics Program .

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Should I get a math PhD for a quant position?

Math major senior at a semi target here. I failed in breaking into a quant intern last summer so I focused on research and two of my papers just got accepted. Getting a PhD in math is among my dream since high school, and now I'd hope to land on a quant position in future, should I consider applying to graduate program in math? Any advice would be greatly appreciated!

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Spark Investment Management (Spark)

Phd-level quantitative trader.

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Job Description

• are good with computers but don’t want to be a pure programmer;
• have the real-world skills to work in different areas and combine these inputs into focused projects and working hypotheses;
• and have exceptional quantitative skills,

then you are a rare breed. You can polish all your fundamental skills and develop even further at Spark while achieving more financial independence than in other opportunities.

As Quantitative Trader, you will work toward implementing quantitative finance solutions.  You will earn outstanding compensation, build your skill base, and tackle the variety of interesting and difficult projects our entrepreneurial environment has to offer.

You will develop real-world expertise in trading to leverage your skills.  The Quantitative Trader will use quantitative skills in a small-team environment while starting at a higher level than in a purely analytical role.  Your work will include complicated computer assignments and statistical analysis but also the next steps toward implementing your analysis and conclusions.  A background in machine learning, optimization, or statistical modeling will be helpful but can also be picked up on the job if you have a strong computer and quantitative base.

As the quant trading sector matures, the difficulty of inventing strategies increases.  In this role, you will have the opportunity to generate more revenue from already-profitable strategies without having to first invent them. If you can bring high-level technical skills and a strong work ethic, along with a desire to improve your general skills, we can help you succeed. 

Job Qualifications

This position requires good computer skills, strong communication skills to work with people in different areas, and exceptional quantitative skills.  A high level of skill in software can compensate for deficits in mathematical knowledge and ability.

Because you will be involved with trading and we value your quantitative and computer skills as well as your other multifaceted abilities, this opportunity is superior both in starting compensation and long-term potential.  This position requires a PhD, and your future career path can specialize or broaden within Spark depending on your inclinations.  When you submit your resume, please include your GPAs and standardized test scores.  If you have awards or medals in mathematical or scientific competitions, be sure to include those in your application materials.  If you are facing a pressing deadline due to other offers, you can contact [email protected] to request consideration before our on-campus date. 

Company Description

The next generation in hedge fund management.  For over a decade, our outstanding people, many of whom have PhDs, have been developing the next generation of technologies, ideas, and strategies.  We have been highly successful because of both our technically superior strategies and our brilliant people.  We have a close-knit group, distinguished by their interest in our shared success, half of whom are graduates from Harvard, MIT, and Cornell.  This lucrative synthesis of human and machine cognition in an intellectually rich, supportive environment is at the core of Spark.  We have experience with H-1B and green card sponsorship, and visa status is not a constraint.  All submissions kept confidential.

www.sparkim.com

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• Education Advice

PhD vs Full Time Quant Researcher

• Thread starter Thread starter alittlebear
• Start date Start date 3/6/21

alittlebear

Hi all, I am a senior student who is about to graduate and now feel a little confused about the future. I want to be a top quantitative researcher and my goal is to enter a top hedge fund. I have two options, one is to work after graduating from a master's degree, and the other is to continue to pursue a PhD degree. I have received some offers suitable for pursuing PhD in Statistics (Uchicago and Duke stat ms) and some offers suitable for pursuing PhD in Finance (Columbia Business School FinEcon ms). If choose to work after graduation, I may need to constantly change jobs to achieve my goals. My question is: 1. I saw many people continue to study PhD after reading MFE and finishing a decent summer intern, so I would like to ask whether the work of a full-time quantitative researcher is really as interesting as what people thought and do they have a steep learning curve? 2. Whether companys like DE Shaw and Citadel would prefer finance PhD or statistics PhD, or both have the opportunity to get into them directly? 3. What is the salary for PhD at the beginning and the salary for 5 years after MFE graduation? Assume that MFE students worked in big bank / top prop trading like IMC. 4. In the long run, is PhD more conducive to career development? I have passion in both finance and statistics so PhD degree is not a problem, and I also hope to learn more through PhD training.  

1. Yeah I’m a researcher at a buy side firm at the moment. Your learning curve is steep depending on what your experience is like. If you know your quantitative methods and techniques then you might have a sharp learning curve on the business side and vice versa. The job is suited for phds not because they have phds, but because they’re capable of learning new things quickly and well because no one holds your hand. If you can learn concepts quickly and maintain immaculate attention to detail, this is a good fit, because the work is great 2. Don’t do a finance PhD so you can get into a citadel. I have an undergrad and have gotten research offers at good prop trading firms and citadel. 3. don’t do a PhD expecting a massive salary boost unless you’re going to a old school firm. You’re going to end up having regrets because most of the top firms now will give an undergrad with 4-6 years work experience in quant research more or the same as a PhD just starting their career (unless you have some truly exceptional research relevant to the firm or some prior work experience) Also IMCs comp isn’t that great, they lock a lot of it away behind deferred comp to force you to stay at the firm 4. I’m the son of two phds and work with phds daily so I can take some sort of stab at this, but I would say yeah depending on what your goal is. If your goal is to just be a quant, then no. If your goal is to maybe be a professor, or a researcher at a PhD only lab, or consult a central bank, or even start your own firm/ strategy then yeah, it will give you the skills and qualifications necessary however given your stated career goals I don’t think a PhD is a good fit. If you’re passionate about learning about finance / stats, you can do it yourself too without taking the 4-6 year hit on your career. Top finance firms like mine or citadel don’t really care how much finance knowledge you’re bringing out of your undergrad because they feel like they can train you. If you don’t think you can get a job in the industry without a masters, then go for the subject that you’re more interested in and get the masters. Definitely don’t pursue a PhD if your end goal is just to be a industry quant unless you’re purely looking to learn  

Kinda unrelated but sorta related to the spirit of your post, but IMO the things that make a good researcher are 1. An ability to pick up new concepts quickly 2. The ability to implement their ideas into production code 3. Having a solid understanding of financial markets. No one is expecting you to know what makes a good trade (unless you’ve got prior trading experience) because a MFE or a PhD aren’t really going to teach you that, but understanding the basic mechanics of the market you’ve chosen to work in (I.e if in credit trading you should know basic bond terminology and bond math, common trading techniques in bonds is a big plus). An MFE will help out with number 3, so if you feel like you have 1&2, post the MFE you’re probably good to go wherever. So you can see from those skills why a PhD does well in the job, but also why someone with just an undergrad or masters can as well. It’s usually the case that the undergrads that make it into these roles hit the three criteria out of passion/ coursework (similar to phds)  

tips said: 1. Yeah I’m a researcher at a buy side firm at the moment. Your learning curve is steep depending on what your experience is like. If you know your quantitative methods and techniques then you might have a sharp learning curve on the business side and vice versa. The job is suited for phds not because they have phds, but because they’re capable of learning new things quickly and well because no one holds your hand. If you can learn concepts quickly and maintain immaculate attention to detail, this is a good fit, because the work is great 2. Don’t do a finance PhD so you can get into a citadel. I have an undergrad and have gotten research offers at good prop trading firms and citadel. 3. don’t do a PhD expecting a massive salary boost unless you’re going to a old school firm. You’re going to end up having regrets because most of the top firms now will give an undergrad with 4-6 years work experience in quant research more or the same as a PhD just starting their career (unless you have some truly exceptional research relevant to the firm or some prior work experience) Also IMCs comp isn’t that great, they lock a lot of it away behind deferred comp to force you to stay at the firm 4. I’m the son of two phds and work with phds daily so I can take some sort of stab at this, but I would say yeah depending on what your goal is. If your goal is to just be a quant, then no. If your goal is to maybe be a professor, or a researcher at a PhD only lab, or consult a central bank, or even start your own firm/ strategy then yeah, it will give you the skills and qualifications necessary however given your stated career goals I don’t think a PhD is a good fit. If you’re passionate about learning about finance / stats, you can do it yourself too without taking the 4-6 year hit on your career. Top finance firms like mine or citadel don’t really care how much finance knowledge you’re bringing out of your undergrad because they feel like they can train you. If you don’t think you can get a job in the industry without a masters, then go for the subject that you’re more interested in and get the masters. Definitely don’t pursue a PhD if your end goal is just to be a industry quant unless you’re purely looking to learn Click to expand...

tips said: Kinda unrelated but sorta related to the spirit of your post, but IMO the things that make a good researcher are 1. An ability to pick up new concepts quickly 2. The ability to implement their ideas into production code 3. Having a solid understanding of financial markets. No one is expecting you to know what makes a good trade (unless you’ve got prior trading experience) because a MFE or a PhD aren’t really going to teach you that, but understanding the basic mechanics of the market you’ve chosen to work in (I.e if in credit trading you should know basic bond terminology and bond math, common trading techniques in bonds is a big plus). An MFE will help out with number 3, so if you feel like you have 1&2, post the MFE you’re probably good to go wherever. So you can see from those skills why a PhD does well in the job, but also why someone with just an undergrad or masters can as well. It’s usually the case that the undergrads that make it into these roles hit the three criteria out of passion/ coursework (similar to phds) Click to expand...

monbuchicassie

alittlebear said: Thanks for the reply! I think I do have the abilities but what I am facing is a more and more competitive job market. I am not from a top undergraduate school so it's pretty hard for me to find jobs in leading buysides after graduation from MFE. It would be pretty struggle for me if I start to work at sell sides. Also, I think most of the applicants are using interview book and leetcode to 'fit' the requirement of a job, instead of truly master the knowledge in statistics/math, which I think is quite boring. I am in awe of knowledge and also know my deficiencies in statistics, that's why I want to have a PhD degree Click to expand...

TheComplexUnit

(1) You'll learn more practical skills on the street than you would in any PhD programme. (2) Hedge funds are not 'maths departments'. In other words, you'd be surprised how easy the maths you'll need is. (3) Getting into so-called "top companies" is not necessarily what you want to aim for. Chances are you'll be pigeon-holed into a highly specialised semi-brain dead role. Aim for smaller companies with a start-up attitude. (4) These days, highly skilled developers with a modicum of quant skills are kings. Again: avoid the delusion that epsilon-delta will bring you glory.  

Since I posted last on this thread I've started becoming more involved with our hiring and here are the four things that make a perfect candidate for us 1. Great c++ / OOP/ software dev skills (can self teach, get from a degree, or from taking the quant net class, all of this + work experience / side projects usually leads to decent developers) 2. Quant skills (linear algebra, probability, common ML libraries, common optimization techniques like normal method, gradient descent, anything else is a plus) 3. Good research ability 4. attitude While no candidate is perfect and has all of those skills, we generally want to see candidates with at least 3 of these pillars before considering an offer. Cannot stress how many "Math whiz" PhDs with strong PDE skills we have not hired because they don't have a strong SWE background. The "gap" in quant skills has definitely diminished as a plethora of strong ML courses / programs have become available to undergraduates, but there are very few quant research candidates with strong software engineering experience. Being a strong python programmer (note the use of programmer, not developer) while helpful for ad-hoc research, is usually not sufficient at a fully automated trading firm  

Daniel Duffy

C++ author, trainer.

What's "SWE background"? What distinguishes "programmer" from "developer"? It may be an idiosyncratic and have several definitions on who you ask. I would say most quants of any flavour should be able to test their ideas (in a computer).  

SWE = software engineering To me, a "programmer" is someone who has a basic to decent grasp of the syntax in some language (i.e knows the syntax for c++ / python ) enough to be self-sufficient at creating their own models and run backtests given enough time (I.e. a recent undergrad in a non-CS degree with some CS courses, someone who has solely taken a quant net class); A programmer might not understand the cost associated with copying vs pass by reference or the usefulness of move, but would understand enough to implement a basic linear regression given enough time A developer is someone who can take that model and architect the libraries / packages / whatever else is needed to productionize that model (keyword here is architect). Being a good architect might mean having a solid grasp of design patterns, a deep enough understanding of the language to understand trade offs of using certain data structures / keywords, the skill to write really good unit tests, and an ability to think about the future of any design choices made (i.e. if part of your model requires implementing something which should be generic to future models, are you implementing it in a generic way so another researcher can use it?) - This is usually someone who at bare minimum the credentials of a "programmer" but also has some work experience and / or a computer science degree from a decent program that teaches these skills. Finding a "strong developer" who has good quant skills is pretty hard IMO, and is part of what makes it so difficult to get a job as a quant researcher at one of the top prop shops / hedge funds that specialize in fully automated trading. Agreed fully that there are many definitions, and many different ways to satisfy the criteria I listed, just how we distinguish when hiring at my firm.  

By the way, what the difference betwen quant researcher and quant analyst? What kinds of skills do the two positions need?  

Cannot stress how many "Math whiz" PhDs with strong PDE skills we have not hired because they don't have a strong SWE background Sounds logical. This happens when pure maths pde (theory only) have not learned to program. So, it would not be a surprise to me. It takes [7,20] years to really learn software design and apply it to anything. Software and programming is a skill to be learned. Many underestimate the task. The period 1970-1990 was the golden period of PDEs and applications to all kinds of stuff, just before the fall of the Wall.  

Daniel Duffy said: Cannot stress how many "Math whiz" PhDs with strong PDE skills we have not hired because they don't have a strong SWE background Sounds logical. This happens when pure maths pde (theory only) have not learned to program. So, it would not be a surprise to me. It takes [7,20] years to really learn software design and apply it to anything. Software and programming is a skill to be learned. Many underestimate the task. The period 1970-1990 was the golden period of PDEs and applications to all kinds of stuff, just before the fall of the Wall. Click to expand...

tips said: Since I posted last on this thread I've started becoming more involved with our hiring and here are the four things that make a perfect candidate for us 1. Great c++ / OOP/ software dev skills (can self teach, get from a degree, or from taking the quant net class, all of this + work experience / side projects usually leads to decent developers) 2. Quant skills (linear algebra, probability, common ML libraries, common optimization techniques like normal method, gradient descent, anything else is a plus) 3. Good research ability 4. attitude While no candidate is perfect and has all of those skills, we generally want to see candidates with at least 3 of these pillars before considering an offer. Cannot stress how many "Math whiz" PhDs with strong PDE skills we have not hired because they don't have a strong SWE background. The "gap" in quant skills has definitely diminished as a plethora of strong ML courses / programs have become available to undergraduates, but there are very few quant research candidates with strong software engineering experience. Being a strong python programmer (note the use of programmer, not developer) while helpful for ad-hoc research, is usually not sufficient at a fully automated trading firm Click to expand...

monbuchicassie said: By the way, what the difference betwen quant researcher and quant analyst? What kinds of skills do the two positions need? Click to expand...

tips said: To me, a quant analyst is really vague because its a term used throughout the job market, not just in finance. In most cases it's someone who is more of a "programmer" with basic to medium quantitative skills, basic to medium research ability; In very rare circumstances, they might do the same work as a researcher (like 15% of the time). Typically, these candidates have at least an undergrad, given that it is typically junior most of these candidates are fresh out of undergrad or a masters program A quant researcher is someone who is more of a "dev" with medium to strong quant skills, strong research ability (aka what most users here typically think of as a quant), though on occasion some jobs might actually be closer to a quant analyst (like 15% of the time, not an exact number this is just a guess based on my intuition); Typically, these candidates have graduate degrees (masters+ some work experience, PhDs, though strong undergraduate candidates can do this as well) Given the vagueness, you should always make sure to ask what the job/title means to whatever firm you're interviewing with since the definitions can be variable. The work of a researcher at an old school firm vs a fully automated firm is very different Click to expand...

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Doctor of Philosophy in Psychology (PhD)

Benjamin Wood

religiosity, moderation, employee religiosity, job satisfaction, leader’s perceived level of religiosity

Disciplines

Leadership Studies | Religion

Recommended Citation

Carney, Brad, "An Examination of Employee's Perceived Level of Their Leader's Religiosity as a Moderator of the Relationship Between an Employee's Religiosity and Job Satisfaction" (2024). Doctoral Dissertations and Projects . 5927. https://digitalcommons.liberty.edu/doctoral/5927

The purpose of this study was to investigate whether an employee’s perceived level of their leader’s religiosity moderates the relationship between an employee’s level of religiosity and job satisfaction. The participants in this research study were recruited through the utilization of a snowball sampling method, primarily leveraging Liberty University’s doctoral student email list and social media platforms such as Facebook and LinkedIn. Participants in the study were required to be 18 and older and had been employed under their current leader for a minimum of one year. The total sample size was N=65. The researcher used a quantitative self-reporting survey approach to data collection using the Huber and Huber (2012) Centrality of Religiosity Scale (CRS-15) survey to measure a leader's level of religiosity as perceived by the employee and an employee's level of religiosity. The Spector (1985) Job Satisfaction Survey (JSS) was used to measure an employee's level of job satisfaction. The data collected from the online CRS-15 and JSS surveys was analyzed employing a correlation research design using linear regression with moderation analysis. The results did not show a significant moderating effect on an employee’s perceived level of their leader’s religiosity. Still, they did find that employees who perceived their leader to have a high level of religiosity reported higher levels of job satisfaction. Furthermore, this study is the first to investigate an employee’s perceived level of their leader’s religiosity and the effect it has on employee job satisfaction.

Since August 29, 2024

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