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Remote Private Equity Data Science Jobs (NOW HIRING)

Mercor

Data Science Expert - Fully Remote

New York, NY · Remote

$70 - $100/hr

Expert Professionals -- AI & Data Science Type: Contract Compensation: $70-$100/hour Location: Remote Commitment: 40 hours/week Role Responsibilities * Guide research and engineering teams to close ...

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Remote Private Equity Data Science information

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$37.5K

$122.7K

$196.5K

How much do remote private equity data science jobs pay per year?

As of Jun 30, 2026, the average yearly pay for remote private equity data science in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What are some of the unique challenges faced by data scientists working remotely in private equity, and how can they be addressed?

Remote data scientists in private equity often encounter challenges such as accessing sensitive financial data securely, collaborating across time zones, and communicating complex analyses to investment teams. To address these, firms typically implement robust cybersecurity protocols, schedule regular virtual meetings to maintain alignment, and use collaborative tools like shared dashboards or project management platforms. Proactively setting clear expectations and maintaining open lines of communication with both technical and non-technical team members are key to success in this fast-paced, data-driven environment.

What is the difference between Remote Private Equity Data Science vs Remote Investment Analyst?

AspectRemote Private Equity Data ScienceRemote Investment Analyst
Required CredentialsDegree in Data Science, Finance, or related fields; proficiency in data analysis toolsDegree in Finance, Economics, or related fields; strong analytical skills
Work EnvironmentCollaborates with data teams, often in tech or finance firms, using data analysis and modelingResearches market trends, evaluates investments, and prepares reports, often in finance firms
Employer & Industry UsagePrivate equity firms, investment funds, consulting firmsAsset management firms, investment banks, hedge funds

Remote Private Equity Data Science focuses on analyzing large datasets to inform investment decisions using advanced analytics, while Remote Investment Analysts evaluate market data and financial reports to recommend investments. Both roles require strong analytical skills but differ in technical focus and daily tasks.

What is Remote Private Equity Data Science?

Remote Private Equity Data Science involves applying data analysis, machine learning, and statistical techniques to support private equity firms in investment decision-making, portfolio management, and risk assessment—all while working remotely. Professionals in this field analyze large datasets, build predictive models, and generate insights to help firms identify valuable investment opportunities and improve operational efficiency. Working remotely allows data scientists to collaborate with global teams and access diverse data sources using cloud-based tools. This role typically requires strong quantitative skills, knowledge of finance, and experience with programming languages such as Python or R.

What are the key skills and qualifications needed to thrive as a Remote Private Equity Data Scientist, and why are they important?

To thrive as a Remote Private Equity Data Scientist, you need strong quantitative analysis skills, proficiency in statistics, and experience with financial modeling, typically supported by a degree in data science, finance, or a related field. Expertise in programming languages like Python or R, familiarity with machine learning libraries, and experience with data visualization tools and databases are commonly required, as are certifications in data science or finance. Exceptional problem-solving abilities, communication skills, and the capacity to work independently and collaboratively in remote settings set top professionals apart. These skills ensure accurate analysis of investment opportunities, clear insights for decision-makers, and effective teamwork across distributed environments.
More about Remote Private Equity Data Science jobs
What cities are hiring for Remote Private Equity Data Science jobs? Cities with the most Remote Private Equity Data Science job openings:
What are the most commonly searched types of Private Equity Data Science jobs? The most popular types of Private Equity Data Science jobs are:
What states have the most Remote Private Equity Data Science jobs? States with the most job openings for Remote Private Equity Data Science jobs include:
What job categories do people searching Remote Private Equity Data Science jobs look for? The top searched job categories for Remote Private Equity Data Science jobs are:
Remote Private Equity Data Science jobs near you
Infographic showing various Remote Private Equity Data Science job openings in the United States as of June 2026, with employment types broken down into 89% Full Time, 9% Part Time, 1% Temporary, and 1% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

Director, Data Science: Data Science Tools

Liberty Information Technology Limited

MA • On-site, Remote

$142K - $201K/yr

Full-time

Posted 13 days ago

Key responsibilities

  • Design and build internal tools, pipelines, and applications that improve model development, evaluation, and deployment.

  • Own strategy and roadmaps for improving data science workflows and tooling across USRM.

  • Design, build, and maintain Python packages used across the organization.

Job description

Description
The Data Science Infrastructure organization within USRM is hiring a Senior Technical Professional, Data Scientist to join the Data Science Tools team. This role will focus on improving the end-to-end modeling workflow for USRM Data Science by building internal tools, pipelines, and applications that streamline model development, evaluation, deployment, and iteration. The ideal candidate is highly technical, proactive, and motivated by building systems that help other data scientists work more efficiently.
**Candidates who live within 50 miles of Boston, MA; Portsmouth, NH; Seattle, WA; Columbus, OH; or Plano, TX will follow a hybrid schedule, coming into the office two days per week. Otherwise, this role is remote with occasional travel. **
Responsibilities:
  • Design and build internal tools, pipelines, and applications that improve model development, evaluation, and deployment
  • Own strategy and roadmaps for improving data science workflows and tooling across USRM
  • Design, build, and maintain Python packages used across the organization
  • Evaluate and implement AI agent capabilities in tooling using approaches such as MCP, RAG, PydanticAI, LangChain, or related frameworks
  • Work with workflow and modeling tools such as Luigi, Airflow, Celery, MLflow, H2O, scikit-learn, Optuna, and LightGBM, as well as Python development tools such as Pydantic, FastAPI, uv, ruff, and pytest
  • Promote MLOps and AI agent best practices in collaboration with groups such as Enterprise Data & Data Science
  • Stay current on developments in open-source data science frameworks, MLOps, and agentic coding practices
  • Help shape the direction of the Tools team and contribute to a culture of ownership, collaboration, and continuous improvement

The ideal candidate will have:
  • Professional experience building and maintaining Python-based data science or Machine Learning tooling used by multiple end users or teams
  • Worked with any of the following in a professional setting: Git, Bash/shell scripting, uv, pre-commit, ruff, pytest, or Pydantic
  • Built, deployed, or maintained workflows or pipelines using any of the following: Airflow, Luigi, Celery, Databricks, or MLflow
  • Implemented or supported AI/LLM-based tooling using frameworks such as PydanticAI, LangChain, MCP, or RAG
  • Developed, reviewed, or maintained internal Python packages, APIs, or data science applications using tools such as FastAPI, Streamlit, Dash, NiceGUI, or Plotly
  • Applied agentic AI techniques in day-to-day development and incorporate AI capabilities directly into tools and applications where they create meaningful value for data scientists

Qualifications
  • Broad knowledge of predictive analytic techniques and statistical diagnostics of models.
  • Advanced knowledge of predictive toolset; reflects as expert resource for tool development.
  • Demonstrated ability to exchange ideas and convey complex information clearly and concisely.
  • Ability to establish and build relationships within and outside the organization.
  • Ability to give effective training and presentations to management and other groups.
  • Ability to use results of analysis to persuade team, department management or senior management to a particular course of action.
  • Broad knowledge of business drivers and market context.
  • Has a value driven perspective with regard to understanding of work context and impact.
  • Competencies typically acquired through a Ph.D. degree (in Statistics, Mathematics, Economics, Actuarial Science or other scientific field of study) and a minimum of 3 years of relevant experience, a Master`s degree (scientific field of study) and a minimum of 6 years of relevant experience or may be acquired through a Bachelor`s degree (scientific field of study) and a minimum of 8 years of relevant experience.

Employees may apply for a new role after completing 12 months of employment in their current position.
Employees should review all role requirements and apply only for positions for which they are eligible. Hiring processes may vary by country, including differences in procedures, requirements, and timelines. For country-specific details, please consult your local recruiting / HR team.
About Us
Pay Philosophy: The typical starting salary range for this role is determined by a number of factors including skills, experience, education, certifications and location. The full salary range for this role reflects the competitive labor market value for all employees in these positions across the national market and provides an opportunity to progress as employees grow and develop within the role. Some roles at Liberty Mutual have a corresponding compensation plan which may include commission and/or bonus earnings at rates that vary based on multiple factors set forth in the compensation plan for the role.
At Liberty Mutual, our goal is to create a workplace where everyone feels valued, supported, and can thrive. We build an environment that welcomes a wide range of perspectives and experiences, with inclusion embedded in every aspect of our culture and reflected in everyday interactions. This comes to life through comprehensive benefits, workplace flexibility, professional development opportunities, and a host of opportunities provided through our Employee Resource Groups. Each employee plays a role in creating our inclusive culture, which supports every individual to do their best work. Together, we cultivate a community where everyone can make a meaningful impact for our business, our customers, and the communities we serve.
We value your hard work, integrity and commitment to make things better, and we put people first by offering you benefits that support your life and well-being. To learn more about our benefit offerings please visit: https://LMI.co/Benefits
Liberty Mutual is an equal opportunity employer. We will not tolerate discrimination on the basis of race, color, national origin, sex, sexual orientation, gender identity, religion, age, disability, veteran's status, pregnancy, genetic information or on any basis prohibited by federal, state or local law.
Fair Chance Notices
  • California
  • Los Angeles Incorporated
  • Los Angeles Unincorporated
  • Philadelphia
  • San Francisco

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