Data Scientist

As a Data Scientist, you will have the following responsibilities:

• Build trusted relationships with business and technical stakeholders; understand their objectives, decision processes, constraints, and definitions of success.
• Lead discovery sessions and translate broad business questions into clear analytical problem statements, hypotheses, deliverables, success metrics, and actionable roadmaps.
• Manage stakeholder expectations through clear communication of scope, assumptions, dependencies, risks, model limitations, progress, and recommended decisions.
• Apply structured analytics problem solving to diagnose issues, identify value drivers, evaluate alternative approaches, and connect findings to practical business actions.
• Collect, clean, integrate, and explore structured and unstructured data from multiple sources to uncover trends, relationships, anomalies, and opportunities.
• Design, develop, and validate data science models using methods such as regression, classification, clustering, forecasting, propensity modeling, optimization, recommendation systems, and natural language processing.
• Engineer features, select algorithms, tune models, and evaluate performance using technically sound metrics aligned to business outcomes.
• Assess model robustness, stability, bias, interpretability, and generalizability; clearly document assumptions, intended use, limitations, and validation results.
• Collaborate with data engineering, product, and technology teams to create reliable analytical datasets, reusable pipelines, deployment workflows, and model monitoring.
• Own project planning and delivery for assigned workstreams, including scope, milestones, timelines, resources, status reporting, and quality control.
• Coordinate cross-functional contributors, manage dependencies, identify delivery risks early, and drive decisions needed to keep projects progressing.
• Translate analytical outputs into clear recommendations, visualizations, presentations, and decision tools for technical and non-technical audiences.
• Measure adoption and business impact after delivery, gather stakeholder feedback, and identify opportunities to improve or scale the solution.
• Contribute reusable code, analytical frameworks, documentation, and best practices that improve the quality and efficiency of data science delivery.

Key Skills & Competencies:

• Stakeholder management: ability to build credibility, facilitate working sessions, influence decisions, resolve competing priorities, and communicate effectively with club, league, product, engineering, analytics, and leadership audiences.
• Analytics problem solving: ability to structure ambiguous questions, form hypotheses, identify relevant data, choose appropriate methods, synthesize evidence, and recommend practical actions.
• Data science model creation: hands-on experience developing, validating, interpreting, and operationalizing predictive, prescriptive, and machine learning models.
• Project management: ability to define scope, build work plans, manage milestones and dependencies, communicate status, and deliver high-quality outcomes across multiple workstreams.
• Strong foundation in statistics, probability, experimental design, causal inference, and machine learning.
• Advanced proficiency in Python and SQL for data preparation, exploratory analysis, modeling, validation, visualization, and automation
• Hands-on experience with libraries and frameworks such as pandas, NumPy, scikit-learn, statsmodels, XGBoost
• Excellent written and verbal communication, with the ability to simplify complex analytical concepts without losing important nuance.

To be successful in this role, you will need the following qualifications:

• Bachelor's degree in data science, statistics, mathematics, computer science, engineering, economics, operations research, or a related quantitative field
• 5+ years of professional experience in data science, applied statistics, machine learning, advanced analytics, or a related role.
• Demonstrated experience independently creating and validating analytical or machine learning models that informed decisions or supported business workflows.
• Proven experience managing stakeholders and delivering cross-functional analytics projects from problem definition through implementation and impact measurement.
• Ability to manage multiple priorities while maintaining analytical quality, clear documentation, and dependable stakeholder communication.