Lead Data Scientist, Predictive Modeling & Causal Inference
About OneSix
OneSix is a leading data and artificial intelligence (AI) consultancy that helps businesses build the strategy, technology, and teams they need to scale growth and efficiency. Its team of skilled Data Engineers, Data Scientists, Machine Learning (ML) Experts, and AI Engineers seamlessly integrate with client teams to solve their most challenging business problems. Leveraging strategic partnerships with Snowflake, AWS, Matillion, Fivetran, Pyramid Analytics, and more, the company uses modern technology, scalable architectures, and industry best practices. With the recent acquisition of Strong Analytics, an ML and AI consultancy, OneSix is a uniquely powerful business partner to the enterprise, with a talent mix that is nearly impossible to find under one roof.
OneSix is a fast-growing firm with significant career opportunities for motivated professionals who want to help create a unique company. We are committed to fostering an inclusive employee experience that reflects the world we live in today. We’re an equal-opportunity employer that welcomes people regardless of backgrounds, experiences, abilities, and perspectives.
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