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Causal Inference Machine Learning Postdoctoral Jobs in Charlotte, NC

LPL Financial

Senior Analyst, Data Science

Fort Mill, SC

$75K - $95K/yr

This is a high-impact, hands-on role for someone who wants to apply classical data science methods - machine learning, statistics, anomaly detection, and causal inference - to consequential problems ...

NorthEastern

Postdoctoral Research Fellow

Charlotte, NC

$54K - $76K/yr

Northeastern University invites applications for a Postdoctoral Research Fellow position in natural ... Strong research background in NLP, machine learning, or closely related areas * Evidence of ...

Scout Motors

Lead, AI Engineering

Charlotte, NC

$100K - $131K/yr

Developing machine learning and generative AI solutions that solve high-impact business problems. * Building scalable AI infrastructure, including model training, deployment, and inference platforms.

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Causal Inference Machine Learning Postdoctoral information

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How much do causal inference machine learning postdoctoral jobs pay per year?

As of Jul 27, 2026, the average yearly pay for causal inference machine learning postdoctoral in Charlotte, NC is $52,961.00, according to ZipRecruiter salary data. Most workers in this role earn between $52,300.00 and $55,200.00 per year, depending on experience, location, and employer.

What is a Causal Inference Machine Learning Postdoctoral researcher?

A Causal Inference Machine Learning Postdoctoral researcher is a scientist who specializes in developing and applying machine learning methods to understand cause-and-effect relationships in data. They typically hold a recent PhD in statistics, computer science, economics, or a related field, and work in academic or industry research settings. Their work involves designing experiments, analyzing complex datasets, and creating models that can infer causal relationships, which are crucial for making robust predictions and informed decisions. This role often collaborates with interdisciplinary teams to apply these techniques to domains such as healthcare, social science, or economics.

What are the key skills and qualifications needed to thrive as a Causal Inference Machine Learning Postdoctoral researcher, and why are they important?

To thrive as a Causal Inference Machine Learning Postdoctoral researcher, you need a strong background in statistics, causal inference methodologies, and advanced machine learning, usually evidenced by a PhD in a relevant field. Familiarity with programming languages such as Python or R, experience using statistical software (e.g., TensorFlow, PyTorch, Stan), and knowledge of causal inference libraries are typically required. Outstanding analytical thinking, problem-solving abilities, and strong communication skills help you collaborate effectively and explain complex concepts to diverse audiences. These skills and qualifications are vital for advancing research, deriving actionable insights from data, and contributing to impactful scientific discoveries.

What are some common challenges faced by Causal Inference Machine Learning Postdoctoral researchers when integrating causal models with real-world data?

Causal Inference Machine Learning Postdoctoral researchers often encounter challenges such as dealing with unobserved confounding variables, ensuring data quality, and addressing biases inherent in observational datasets. Integrating advanced machine learning techniques with causal inference frameworks requires careful consideration of model assumptions and validation methods. Collaboration with domain experts is essential to properly interpret results and to translate findings into actionable insights, especially in interdisciplinary settings like healthcare or social sciences.

What is the difference between Causal Inference Machine Learning Postdoctoral vs Data Scientist?

AspectCausal Inference Machine Learning PostdoctoralData Scientist
Required CredentialsPhD in statistics, machine learning, or related fieldBachelor's or Master's in data science, computer science, or related field
Work EnvironmentAcademic research, research labs, universitiesCorporate, tech companies, startups
Industry UsageResearch, academia, specialized industry projectsBusiness analytics, product development, data-driven decision making
Common Search/ComparisonYesYes

The main difference is that Causal Inference Machine Learning Postdoctoral roles focus on academic research and developing new methods in causal inference, often requiring a PhD. Data Scientists typically work in industry, applying existing models to solve business problems, with a focus on data analysis and visualization. While both roles involve machine learning, the postdoctoral position emphasizes research and theory, whereas data science emphasizes practical application.

What are popular job titles related to Causal Inference Machine Learning Postdoctoral jobs in Charlotte, NC? For Causal Inference Machine Learning Postdoctoral jobs in Charlotte, NC, the most frequently searched job titles are:
What job categories do people searching Causal Inference Machine Learning Postdoctoral jobs in Charlotte, NC look for? The top searched job categories for Causal Inference Machine Learning Postdoctoral jobs in Charlotte, NC are:
What cities near Charlotte, NC are hiring for Causal Inference Machine Learning Postdoctoral jobs? Cities near Charlotte, NC with the most Causal Inference Machine Learning Postdoctoral job openings:
Causal Inference Machine Learning Postdoctoral jobs near you
Senior Analyst, Data Science

Senior Analyst, Data Science

LPL Financial Holdings, Inc.

Fort Mill, SC • On-site

$75K - $95K/yr

Other

Medical, Retirement, PTO

Posted 18 days ago

LPL Financial rating

7.5

Company rating: 7.5 out of 10

Based on 69 frontline employees who took The Breakroom Quiz

117th of 150 rated financial services

Job description

Where Ambition Meets Innovation
Build a career that matches all your initiative with an impressive dose of innovation. From cutting-edge resources and a collaborative environment to the freedom to make an impact and more, you'll find the ingredients you need at LPL Financial to shape your success while helping clients pursue their financial goals.
Job Overview:
We are seeking a curious and analytically rigorous Senior Analyst, Data Science to design and build models, analyses, and decision-support tools that drive transformation across the firm's home-office functions -Service, Operations, Supervision, Compliance, Legal, and Risk. This role sits on a small, high-leverage data science team within our Data Analytics & Reporting organization, chartered to deliver trusted, AI-enabled insights that drive measurable business outcomes for a Fortune 500 broker-dealer.
You'll closely collaborate with the team and key business partners to frame analytical problems, design and execute analyses, and translate results into actionable recommendations. This is a high-impact, hands-on role for someone who wants to apply classical data science methods - machine learning, statistics, anomaly detection, and causal inference - to consequential problems in a regulated environment, where the quality of a model depends as much on understanding the business and regulatory context as it does on the math.
Roles & Responsibilities:
Insight Generation & Analysis
  • Design and execute end-to-end analyses that surface meaningful business insights, from data extraction and cleaning through modeling and interpretation.
  • Apply statistical methods - including hypothesis testing, regression, and causal inference - to answer business questions with the rigor and clarity expected in a regulated environment.
  • Translate complex analytical outputs into clear narratives and visualizations for business stakeholders and senior leadership.

Machine Learning & Modeling
  • Build, validate, and deploy supervised and unsupervised machine learning models supporting use cases such as risk tiering, surveillance and alert prioritization, anomaly detection, segmentation, and workload/cost-to-serve modeling.
  • Evaluate model performance using appropriate metrics and clearly communicate trade-offs, assumptions, and limitations to both technical and non-technical audiences.
  • Stay current on advances in applied ML and bring emerging methods to bear on relevant business problems.

Causal Inference & Experimentation
  • Design and analyze A/B tests and observational studies to identify causal relationships and measure the impact of business initiatives.
  • Apply quasi-experimental methods when randomized experiments are not feasible.
  • Partner with business teams to build a culture of evidence-based decision-making.

Data & Collaboration
  • Work closely with data engineers, product managers, business stakeholders, and subject matter experts to access, understand, and leverage data assets across the enterprise.
  • Document analytical workflows, assumptions, code, and findings to ensure reproducibility, knowledge sharing, and audit readiness.
  • Contribute to building a scalable data science practice by identifying opportunities to improve tools, processes, and methodologies.

What we are looking for?
We are looking for strong collaborators who deliver exceptional client experiences and thrive in fast-paced, team-oriented environments. Our ideal candidates pursue greatness, act with integrity, and are driven to help our clients succeed. We value those who embrace creativity, continuous improvement, and contribute to a culture where we win together and create and share joy in our work.
We are also looking for someone whose experience already maps closely to the kind of work this team does. The most impactful data scientists will already be fluent in the business and regulatory context of a broker-dealer - who understand how the firm's front- and back-office functions interact, how operational and supervisory workflows are structured, how regulatory obligations shape the way work is done, and how home-office professionals across functions like Service, Operations, Supervision, Compliance, Legal, and Risk actually use analytics in their day-to-day work. That kind of fluency is hard to acquire on the job and dramatically shortens the time to meaningful contribution. Candidates who bring it will find themselves working at the leading edge of the team's portfolio almost immediately.
Requirements:
  • 3+ years of experience in data science, quantitative analysis, or applied research role in a business setting.
  • Bachelor's degree in Statistics, Mathematics, Computer Science, Economics, Data Science, or a related quantitative field required
  • Experience with Python for data manipulation, statistical analysis, and machine learning that goes beyond Jupyter notebooks; strives for clean, Git version-controlled code.
  • Experience working with large-scale data in SQL & Snowflake; comfortable building and maintaining clean, reproducible data pipelines as needed to support modeling and analysis work.

Core Competencies:
  • Solid grounding in statistics, probability, and machine learning fundamentals.
  • Hands-on experience with causal inference methods and experimental design.
  • Exposure to anomaly detection techniques applied to surveillance, fraud, or risk problems.
  • Experience working with large-scale data in SQL & Snowflake; comfortable building and maintaining clean, reproducible data pipelines as needed to support modeling and analysis work.
  • Data visualization skills and the ability to communicate findings clearly to non-technical stakeholders; note this role will not be focused on developing dashboards..

Preferences:
  • Direct experience as a data scientist or quantitative analyst inside a FINRA-registered broker-dealer, with hands-on work supporting one or more home-office functions such as Service, Operations, Supervision, Compliance, Legal, or Risk.
  • Working knowledge of the regulatory framework that governs broker-dealer activity (SEC, FINRA, state securities regulators) and an appreciation for how that framework may influence the design of data science solutions that ensure our stakeholders can continue to meet their regulatory obligations
  • Active FINRA registration (e.g., Series 7, Series 24, Series 99) is unusual for a data science candidate and would be considered a meaningful differentiator.

Pay Range:
$87,756.00 - $146,260.00
Actual base salary varies based on factors, including but not limited to, relevant skill, prior experience, education, base salary of internal peers, demonstrated performance, and geographic location. Additionally, LPL Total Rewards package is highly competitive, designed to support your success at work, at home, and at play - such as 401K matching, health benefits, employee stock options, paid time off, volunteer time off, and more. Your recruiter will be happy to discuss all that LPL has to offer!
Company Overview:
LPL Financial Holdings Inc. (Nasdaq: LPLA) is among the fastest growing wealth management firms in the U.S. As a leader in the financial advisor-mediated marketplace(6) , LPL supports over 32,000 financial advisors and the wealth management practices of approximately 1,100 financial institutions, servicing and custodying approximately $2.3 trillion in brokerage and advisory assets on behalf of approximately 8 million Americans. The firm provides a wide range of advisor affiliation models, investment solutions, fintech tools and practice management services, ensuring that advisors and institutions have the flexibility to choose the business model, services, and technology resources they need to run thriving businesses. For further information about LPL, please visit ;br>
At LPL, independence means that advisors and institution leaders have the freedom they deserve to choose the business model, services, and technology resources that allow them to run a thriving business. They have the flexibility to do business their way. And they have the freedom to manage their client relationships, because they know their clients best. Simply put, we take care of our advisors and institutions, so they can take care of their clients.
For further information about LPL, please visit ;br>
Join the LPL team and help us make a difference by turning life's aspirations into financial realities. Please log in or create an account to apply to this position. Principals only. EOE.
Information on Interviews:
LPL will only communicate with a job applicant directly from an @lplfinancial.com email address and will never conduct an interview online or in a chatroom forum. During an interview, LPL will not request any form of payment from the applicant, or information regarding an applicant's bank or credit card. Should you have any questions regarding the application process, please contact LPL's Human Resources Solutions Center at .
EAC 5.19.26

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