Job Description

Requirements:

• Degree in Computer Science or a related field (Master's Degree preferred but not required)
• Graduate-level mathematical training (e.g., undergraduate degree in a quantitative subject)
• 3+ years of experience in a Data Scientist role or similar, focusing on data analysis, visualization, Machine Learning, and AI
• Proficient in Python and object-oriented programming principles
• 3+ years of experience with Tableau, PowerBI, or similar dashboard tools
• 3+ years of experience with SQL
• Strong experience with cloud platforms (AWS, Azure, GCP) and cloud-based data services (e.g., AWS Athena, Redshift, Google BigQuery)
• Solid understanding of data modeling
• Experience with version control systems (e.g., Git)
• Experience delivering Data Science, Machine Learning, and AI projects in production environments using agile methods
• Ability to present insights effectively through dashboarding tools like Tableau or Looker Studio
• Knowledge of data governance, security, and privacy practices
• Strong problem-solving, research, and troubleshooting skills for complex data issues
• Excellent communication and collaboration skills for cross-functional teams
• Ability to work in fast-paced environments, manage multiple priorities, and meet deadlines
• Experience with complex data sets and providing actionable insights
• Strategic thinking, multitasking, and a collaborative work approach
• Experience with coaching and mentoring team members
• Experience with API development for exposing ML/AI models (e.g., FastAPI)
• Experience working remotely (desirable)
• Interest or experience in behavior change theories (desirable)
• Full Data Science development lifecycle experience
• Experience with sensitive, healthcare-related data (desirable)
• Familiarity with Figma (optional)

• Evaluate and implement new technologies in Data Science, Machine Learning, and AI.
• Contribute to the development and execution of the Data Science strategy.
• Provide evidence-based recommendations to guide strategic program implementation.
• Measure and analyze project impact to assess goal achievement.
• Drive data collection and refine existing data sources.
• Collaborate on MLOps best practices, including CI/CD, model monitoring, and production scaling with DataOps Engineering.
• Work with DataOps Engineering to build automated impact measurement frameworks (e.g., A/B testing, causal inference).
• Foster cross-disciplinary collaboration within the team to extract value from diverse skill sets.
• Collaborate with multidisciplinary teams to implement programs effectively.
• Partner with Monitoring and Evaluation, Strategy, Experience Design, Engineering, and SRE teams to develop monitoring tools for impact measurement.
• Define and maintain KPIs to track the success of data initiatives.

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