Lead Data Scientist

We’re looking for a Lead Data Scientist who sets technical direction for a workstream, goes deep on the

modeling and architecture decisions personally, and leads a small team of data scientists/engineers. You’ll

be the senior technical authority the client turns to when a model decision needs defending.

What you’ll do

• Own the full model lifecycle end to end: problem framing, feature engineering, model architecture

selection, training at scale, validation, deployment, and post-launch monitoring and retraining

• Make and defend architecture-level calls: which model family, how much complexity is actually

justified by the data and the business case, when a classical model beats a deep learning one and

vice versa

• Design and run rigorous experiments (A/B tests, causal inference, uplift modeling) and be able to

explain confounders and why an offline metric lied to you

• Build and own feature pipelines and training infrastructure that hold up at production scale and

under data drift

• Diagnose model degradation in production and make the retrain-versus-redesign call, including

rollback plans

• Set the technical bar for the team: code review standards, experiment tracking, model validation

rigor, and mentor 2-4 data scientists/engineers against it

• Be the primary technical point of contact for the client, translating ambiguous problems into scoped

• 10-12 years of experience in data science, with demonstrated ownership of models from problem

definition through production impact and measured business outcome * Deep, defensible grounding

across model families: classical ML (regression, tree ensembles like XGBoost/LightGBM) and

deep learning (architecture choice, training at scale), with clear judgment on when each is the right

call

• Domain depth in at least one deep learning area relevant to enterprise work: NLP, forecasting,

recommendation systems, or computer vision, snowflake including hands-on architecture and

training decisions, not just fine-tuning a pretrained model

• Production-grade feature engineering and training infrastructure experience, including experience

with distributed training or large-scale compute (Spark, Ray, or equivalent)

• Experience owning a model in production long-term: monitoring, drift detection, retraining triggers,

rollback

• Experience leading a team technically, including mentoring and setting the standard for others’

modeling and code work

• Strong client-facing communication, able to hold a technical argument with a client stakeholder

and explain a model’s limitations plainly

• cloud/ML platform stack - Databricks, SageMaker, Vertex AI, Azure ML, etc.

• GenAI/LLM applied experience: retrieval design, evaluation harnesses, honesty about failure

modes, not just demo projects

• MLOps tooling depth: MLflow or similar model registries, automated retraining pipelines, CI/CD

for ML

• Prior consulting or professional services background, comfortable across multiple concurrent client

engagements

Primary Skills

• Classical ML - regression, classification, tree ensembles (XGBoost/LightGBM), model selection

judgment

• Snowflake - Should be genuine hands-on experience working on Snowflake Platform.

• Deep learning - architecture design and training at scale in at least one domain area (NLP,

forecasting, recommendation systems, or computer vision)

• Causal inference / experimentation - A/B testing, uplift modeling, confounderaware analysis

• Feature engineering & training infrastructure - production-scale pipelines, distributed

compute (Spark, Ray, or equivalent)

• MLOps / production ownership - deployment, drift and degradation monitoring, retraining

triggers, rollback

• Technical leadership - mentoring, code/model review standards, setting team technical bars.

• Client communication - defending model tradeoffs and limitations to nontechnical stakeholders

• Python and SQL - production-grade