Lead Data Scientist, Predictive Modeling & Causal Inference

Lead Data Scientist

We're looking for a Lead Data Scientist to embed with key clients as a senior technical partner on their data science team. This is a player-coach role at the intersection of rigorous predictive modeling and production engineering: someone who is as comfortable deriving a causal estimate or specifying a generalized linear model as they are debugging a Spark job.

You'll work closely with the client's data science team to shape how the organization understands and predicts user behavior and business outcomes. Success in this role depends as much on the strength of your judgment as your ability to earn trust in a room.

Comfort in consulting work is also a requirement, working with production systems that have grown organically over years, data that isn't always clean, and business stakeholders who need answers on a timeline. You should find that kind of complexity energizing rather than draining.

What You'll Do

• 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.

What You Bring

• 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. (Strong candidates with somewhat less experience but exceptional depth are still encouraged to apply.)
• 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.

Nice to Have

• 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.

Compensation / Benefits

• Competitive compensation
• 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

This position offers a base salary in the range of $180,000–$200,000 USD annually, depending on experience and location. Compensation may vary based on factors including geographic location, level of experience, skills, and performance. This salary range reflects base pay only and does not include any additional compensation such as bonuses, equity, or benefits.

OneSix provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, familial status, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.