Note: The job is a remote job and is open to candidates in USA. OneSix is a data and artificial intelligence consultancy that helps businesses build the strategy, technology, and teams needed to scale growth and efficiency. The Lead Data Scientist will serve as a senior technical partner for client data science teams, designing and deploying predictive models, applying causal inference methods, and translating business questions into production-ready solutions. The role also involves mentoring through a player-coach approach, collaborating with stakeholders, and improving production reliability.
Responsibilities
- Design, build, and validate predictive models, from GLMs and causal/econometric methods to deep learning-based forecasting, to answer questions about user behavior, retention, and business performance
- Apply causal inference techniques (quasi-experimental design, uplift modeling, propensity methods, and related econometric tools) to move client stakeholders beyond correlation and toward decisions they can act on with confidence
- Own the full lifecycle of your models: from exploratory analysis and feature engineering through deployment, monitoring, and retraining in a live production environment
- Work fluently across the stack, writing production-grade SQL, processing data at scale in Spark, and building and deploying models in Python to get from idea to shipped solution without waiting on a hand-off
- Partner directly with the client's data science and broader analytics team, translating ambiguous business questions into well-scoped modeling problems and pushing back, respectfully and with evidence, when the data leads somewhere unexpected
- Communicate technical work clearly to both technical and non-technical stakeholders, building the kind of credibility that earns you a seat at the table on strategic decisions, not just implementation ones
- Bring engineering discipline to a production environment that is mature but imperfect, improving reliability and maintainability incrementally
Skills
- 7+ years of hands-on experience in predictive analytics, applied statistics, or machine learning, with a track record of taking models from concept into production
- Deep fluency in predictive modeling techniques spanning generalized linear models, econometric methods, causal inference, and time-series forecasting, including deep learning-based forecasting approaches with the judgment to speak to trade-offs and failure modes from experience, not just theory
- Strong software engineering fundamentals: you've deployed and maintained models in production, not just prototyped them in a notebook, and you're comfortable owning code quality, testing, and monitoring for the solutions you build
- Proficiency across the modern data stack (e.g., SQL, Spark, and Python) and the judgment to work effectively in a production environment that's mature but occasionally messy, without losing momentum
- Excellent communication and interpersonal skills. You'll be working alongside smart technical leaders, and you need to be able to build trust quickly, hold your ground when you have good reason to, and adapt when you don't. Keen client/stakeholder capability is important
- A graduate degree (M.S. or Ph.D.) in a quantitative or behavioral field ( statistics, economics, computer science, cognitive science, or a related discipline) or equivalent demonstrated experience
- Based in the US or Canada
- Experience modeling user behavior as it relates to downstream outcomes like churn, lifetime value, engagement, or propensity to convert are all directly relevant
- A Ph.D. in cognitive science, behavioral economics, or a similarly human-behavior-oriented quantitative field
- Prior consulting or professional services experience, particularly in client-facing technical roles
Benefits
- Company-paid medical, vision, dental, and wellness benefits for employees
- Company-provided home office equipment
- Flexible vacation and sick days
- Team-oriented and supportive working environment
- Company-sponsored events and swag
Company Overview