Data Scientist

A data scientist uses data, statistics, and programming to help projects make better decisions and build predictive models that solve our clients’ problems. The data scientist works across the full data lifecycle, from collecting raw data to communicating insights to stakeholders in clear, actionable ways. Additionally, this role is  responsible for exploring data using visualization libraries and business intelligence tools to identify relationships, trends, and anomalies that might not be obvious from raw data alone. These visualizations support both exploratory analysis—helping the data scientist generate hypotheses—and explanatory analysis—helping project teams quickly grasp key insights so they can make informed decisions.

All duties will be in service of Nava's values: Be Active Stewards, Delivery over Dogma, Build Together, Innovation Requires New Perspectives, and Progress Takes Work.

• →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

• 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.