Work directly with stakeholders (engineers, program managers, subject matter experts) to scope problems, formulate the right analytical questions, and translate business challenges into rigorous data science approaches
Apply deep analytical thinking to decompose complex problems—critically evaluate data quality and relevance, challenge assumptions, and design methods that address the business need
Develop statistical models, machine learning solutions, and analytical frameworks that deliver actionable insights and drive operational decisions
Build user‑friendly, production‑grade ML/AI applications (e.g., Streamlit, Dash) and analytical artifacts that provide data insights to teams across the enterprise and enable better decision making
Write production-quality Python code and develop analytical pipelines using cloud-based platforms (AWS, Databricks) to support scalable and reproducible data science workflows
Deliver insights through multiple channels—executive recommendations, analytical reports, interactive dashboards, and direct consultation with business leaders
Take ownership of the technical quality and business impact of your work—make thoughtful methodological decisions, validate approaches rigorously, and stand behind your recommendations
Stay current on analytical methods, statistical techniques, and domain-specific best practices to continuously improve the quality and impact of your work

Requirements

Must have a minimum of 5 years of hands-on experience in data science, data analysis, or other relevant professional experience with a Bachelors Degree OR 3 years of hands-on experience in data science, data analysis, or other relevant professional experience with a Masters Degree
Must have a minimum of 8 years of hands-on experience in data science, data analysis, or other relevant professional experience with a Bachelors Degree OR 6 years of hands-on experience in data science, data analysis, or other relevant professional experience with a Masters Degree
Must have strong proficiency with Python, SQL, and Git
Must have deep understanding of statistical methods, machine learning algorithms, and when to apply different analytical techniques
Must have experience developing and deploying machine learning models in production environments
Must have proven ability to translate complex business problems into rigorous analytical frameworks
Must have demonstrated problem‑solving and critical‑thinking skills with an ability to handle complex analytical challenges
Must have excellent communication skills and ability to deliver compelling, actionable recommendations to non‑technical stakeholders
Must have strong track record of ownership and accountability for technical decisions and project outcomes

Benefits

Health insurance coverage
Life and disability insurance
Savings plan
Company paid holidays
Paid time off (PTO) for vacation and/or personal business

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

data sciencedata analysisstatistical modelsmachine learninganalytical frameworksPythonSQLGitanalytical pipelinescloud-based platforms

Soft Skills

analytical thinkingproblem-solvingcritical thinkingcommunicationownershipaccountabilitystakeholder engagementmethodological decision-makingtranslating business problemsdelivering recommendations