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Python Analytics Jobs in Massachusetts (NOW HIRING)

Worldpay

Senior Python Software Engineer

Boston, MA · On-site

$119K - $179.75K/yr

You'll be joining a small team working on expanding a new reporting and analytics product to drastically simplify the payments ecosystem for ecommerce merchants. What you'll own as the Senior Python ...

Covetit

Data Engineer (DBT, Snowflake, Python)

Boston, MA

$124.40K - $149.40K/yr

Collaborate with data analysts, business teams, and stakeholders to understand data requirements ... Strong expertise in Python for data processing and pipeline development * Good understanding of SQL ...

Athenahealth

Manager, Analytics & Insights

Boston, MA · On-site

$133K - $227K/yr

The team partners with Product, Finance, Operations, and HR stakeholders and uses tools such as SQL, Python, Excel, BI platforms, and Snowflake to support analysis and reporting. The team values ...

Athenahealth

Manager, Analytics & Insights

Boston, MA · On-site

$99K - $167K/yr

The team partners with Product, Finance, Operations, and HR stakeholders and uses tools such as SQL, Python, Excel, BI platforms, and Snowflake to support analysis and reporting. The team values ...

ShiftCode Analytics

Data Engineer

Boston, MA

$124.40K - $149.40K/yr

Programming skills in Python for data manipulation, analysis, and modeling. * Experience with distributed computing frameworks like Spark for big data processing. * Knowledge of machine learning ...

ProcDNA

Sr Analyst - Analytics

Boston, MA

$95.60K - $126.60K/yr

Utilize SQL and Python to work with data warehouse sources such as claims and SD data for advanced analytics and business problem-solving. * Process Optimization: Streamline recurring tasks by ...

Pwc

GenAI Python Systems Engineer-Manager

Boston, MA

$99K - $232K/yr

Industry/Sector Not Applicable Specialism Data, Analytics & AI Management Level Manager & Summary ... Python and SQL - Experience with Docker and containerized deployments - Skilled in AI techniques ...

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Python Analytics information

What are the key skills and qualifications needed to thrive as a Python Analytics professional, and why are they important?

To thrive as a Python Analytics professional, you need a strong background in statistics, data analysis, and proficiency in Python programming, often supported by a degree in computer science, mathematics, or a related field. Familiarity with data analytics libraries (such as pandas, NumPy, and scikit-learn), data visualization tools, and experience with databases are typically required. Strong problem-solving, communication, and critical thinking skills help in interpreting data and conveying insights to stakeholders. These abilities are crucial for turning complex data into actionable business decisions and driving organizational success.

What are some typical challenges faced by professionals in Python Analytics roles, and how can I prepare for them?

Professionals in Python Analytics roles often encounter challenges such as handling large and complex datasets, ensuring data quality, and communicating insights effectively to non-technical stakeholders. To prepare, it's beneficial to strengthen your skills in data cleaning, visualization libraries (like Matplotlib or Seaborn), and learn best practices for writing efficient, reproducible code. Collaborating closely with data engineers, business analysts, and decision-makers is also a key part of the job, so developing strong communication and teamwork abilities will help you succeed.

What is a Python Analytics professional?

A Python Analytics professional is someone who uses the Python programming language to collect, process, analyze, and interpret data in order to help organizations make data-driven decisions. They often work with large datasets, perform statistical analyses, create data visualizations, and build predictive models. These professionals may work in industries such as finance, healthcare, marketing, or technology, and typically use libraries like Pandas, NumPy, and Matplotlib. Their work helps businesses gain insights, optimize processes, and solve complex problems through data.

What is the difference between Python Analytics vs Data Analyst?

AspectPython AnalyticsData Analyst
Required SkillsPython programming, data manipulation, statistical analysisExcel, SQL, basic statistics
CertificationsPython certifications, data analysis coursesNone typically required, but certifications like CAP or Microsoft certifications are common
Work EnvironmentData science teams, analytics departments, tech companiesBusiness units, marketing, finance, consulting firms
ToolsPython libraries (Pandas, NumPy, scikit-learn)Excel, SQL, Tableau, Power BI

Python Analytics involves using Python programming to perform advanced data analysis, modeling, and automation, often requiring coding skills. Data Analysts focus on interpreting data using tools like Excel and SQL, providing reports and insights. While both roles analyze data, Python Analytics typically involves more technical and programming expertise, making it suitable for complex data projects and predictive modeling.

What are popular job titles related to Python Analytics jobs in Massachusetts? For Python Analytics jobs in Massachusetts, the most frequently searched job titles are:
What job categories do people searching Python Analytics jobs in Massachusetts look for? The top searched job categories for Python Analytics jobs in Massachusetts are:
What cities in Massachusetts are hiring for Python Analytics jobs? Cities in Massachusetts with the most Python Analytics job openings:
Python Analytics jobs near you
Infographic showing various Python Analytics job openings in Massachusetts as of May 2026, with employment types broken down into 91% Full Time, 6% Part Time, 1% Temporary, and 2% Contract. Highlights an 79% Physical, 6% Hybrid, and 15% Remote job distribution.

Ph.D. Graduate Intern - Quantitative Portfolio Risk Analytics

Risk Analytics Company

Cambridge, MA • On-site

Full-time

Posted 27 days ago

Job description

Ph.D. Graduate Intern – Quantitative Portfolio Risk Analytics (Cross-Disciplinary)

Position Overview
We are seeking an exceptional Ph.D. graduate student to join our team as a Quantitative Portfolio Risk Analytics Intern. This role focuses on developing and applying advanced analytical methods to understand portfolio risk, market structure, and complex financial systems.
We are intentionally recruiting from cross-disciplinary, research-driven backgrounds. Doctoral candidates from fields such as physics, astrophysics, math, applied mathematics, statistics, engineering, economics, computer science, quantum computing, biotech, and other data-intensive sciences are strongly encouraged to apply—especially those interested in translating rigorous quantitative methods into real-world financial applications.
Key Responsibilities
  • Develop and enhance quantitative models for portfolio risk, including factor-based and statistical approaches 
  • Analyze large, high-dimensional financial datasets to uncover structure, dependencies, and sources of risk 
  • Design and implement analytical tools and pipelines using Python and SQL 
  • Contribute to model validation, backtesting, and performance evaluation 
  • Collaborate with risk, engineering, and data teams to improve model scalability and data infrastructure 
  • Communicate complex quantitative insights through clear visualizations and technical summaries 
  • Apply advanced methodologies from your discipline (e.g., stochastic modeling, optimization, machine learning, or geometric/topological approaches) to improve risk analytics 
Required Qualifications
  • Currently enrolled in a graduate Ph.D. program in a highly quantitative field (e.g., Math, Applied Mathematics, Physics, Astrophysics, Statistics, Computer Science, Engineering, Financial Engineering, Economics, Biotech or other data-driven disciplines) 
  • Strong foundation in probability, statistics, and numerical methods 
  • Proficiency in Python (NumPy, pandas, or similar) and/or SQL 
  • Experience working with large datasets and implementing quantitative models 
  • Ability to think rigorously about complex systems and translate theory into practical solutions 
Preferred Qualifications
  • Familiarity with quantitative finance concepts (e.g., portfolio theory, factor models, volatility modeling, Value-at-Risk) 
  • Experience with scientific computing, optimization, or machine learning 
  • Background or research in cross-disciplinary areas such as: 
    • Statistical physics, complex systems, or network theory 
    • Applied or computational mathematics 
    • Machine learning or probabilistic modeling 
    • Quantum computing or advanced optimization techniques 
    • Topological data analysis or geometric data methods 
  • Prior research, publications, or project work demonstrating advanced quantitative modeling 
What You’ll Gain
  • Exposure to real-world portfolio risk problems at the intersection of finance and advanced analytics 
  • Opportunity to apply cutting-edge academic methods in a production environment 
  • Collaboration with a highly quantitative, cross-disciplinary team 
  • Experience working with large-scale financial data and modern analytics infrastructure 
  • Mentorship and potential pathway to full-time quantitative roles 
Duration & Compensation
  • Internship: Summer 2026, with potential to extend 
  • Paid internship (competitive, based on experience and location)