Data Scientist

What you'll do

• Work with stakeholders to understand business problems and define data-driven questions to answer.​
• Collect, clean, and transform large structured and unstructured datasets from multiple sources.​
• Explore data to find patterns, trends, and anomalies using statistical analysis and visualization.​
• Build, evaluate, and deploy machine learning and statistical models to make predictions or automate decisions.​
• Communicate findings through dashboards, reports, and presentations to both technical and non-technical audiences.​
• Participating in business development activities
• Participating in client stakeholder meetings related to growth
• Attending conferences and networking with potential clients
• Reviewing strategy plans and outlining technical requirements for potential work
• Hiring and growing data scientist talent

Required skills

• 5+ years of experience in data science or applied ML roles
• Experience owning end-to-end analytics from discovery to modeling to storytelling to implementation
• Experience working with large, complex datasets across multiple sources
• Strong proficiency in Python 
• Ability to design, train, evaluate, and operationalize ML models at production scale
• Builds and ships advanced statistical/ML models and pipelines using tools like Python and SQL in production
• Strong grasp of probability theory, random variables, common distributions, expectation, variance, and covariance.​
• Solid training in estimation and inference: confidence intervals, likelihood, maximum likelihood estimation, and Bayesian basics
• Ability to choose appropriate statistical techniques for messy, real-world data and explain trade-offs, limitations, and uncertainties.​
• Track record of translating statistical findings into clear, decision-ready narratives and recommendations for stakeholders.​
• Ability to stay current with new methods and tools and apply them thoughtfully to real-world problems.
• Ability to create compelling visualizations (Tableau, Looker, matplotlib, Plotly) that clearly communicate patterns, trends, and insights to both technical and non-technical audiences.
• Mastery of probability theory, statistical inference, linear algebra, and optimization.
• Extensive experience designing and interpreting experiments (including A/B tests and causal inference methods such as DiD, IVs, and matching).
• Strong intuition for model assumptions, uncertainty, and bias/variance tradeoffs; fluency with a broad range of statistical and machine-learning models and diagnostics.