We are seeking a visionary and technically grounded Senior Data Scientist, Enterprise Data & Analytics to lead the development, improvement, and long-term sustainability of our data science models. In this role, you will bridge technical execution and strategic vision, translating complex educational and operational challenges into robust predictive architectures. Beyond building models, you will serve as a foundational mentor, championing technical best practices, elevating the team’s capabilities, and fostering a culture of continuous learning and data science excellence.

Key Responsibilities:

• Model Development & Innovation: Design, develop, and deploy end-to-end machine learning and statistical models to address critical institutional challenges (e.g., student performance trajectories, resource allocation, and operational efficiency).

• Sustainment & Optimization: Audit, monitor, and continuously improve existing production models, ensuring their accuracy, reliability, and long-term scalability.

• Technical Mentorship: Act as a primary mentor to data analysts and engineers, conducting code reviews, organizing internal knowledge-sharing sessions, and disseminating current data science methodologies across the team.

• Data Strategy & Querying: Architect efficient data extraction workflows and construct complex, scalable queries to manipulate large datasets for feature engineering and analysis.

• Collaboration: Work alongside data engineers and product owners to establish clean data pipelines and translate model outputs into actionable dashboards or software features.

What We Are Looking For (Requirements)

• Experience: 5+ years of professional experience in data science, predictive modeling, or advanced analytics, with a proven track record of bringing machine learning models into production environments.

• Expert Python Proficiency: Deep expertise in Python and its core data science ecosystem (including pandas, NumPy, scikit-learn, and related framework libraries) for building robust, clean, and reusable code.

• Advanced SQL Skills: Exceptional ability to write, optimize, and debug complex SQL queries against large relational databases or modern cloud data warehouses.

• Core Data Science Fundamentals: Strong command of statistical modeling, regression techniques, classification algorithms, and experimental design (A/B testing).

• Mentorship Mindset: Prior experience or a strong demonstrated desire to guide, mentor, and upskill junior team members while establishing technical standards.

Nice to Haves (Preferred Qualifications)

• Statistical Programming in R: Familiarity with R for exploratory data analysis, prototyping, or specific statistical packages.

• Cloud & MLOps Tools: Exposure to cloud data infrastructure (such as AWS, GCP, or Snowflake) and basic ML orchestration/versioning tools (e.g., MLflow, Airflow, Git).

• Domain Context: Prior experience working within education, non-profits, or public sector datasets, though a diverse background across other industries is highly valued.