Lead Data Scientist - Data Cloud Acceleration
- Frame & focus. Translate fuzzy growth ideas into moldeable problems, pick the metrics that matter, and design bite-sized experiments to learn quickly.
- Build fast, in or out of the box. Finetune a foundation model when it's the 80percent solution; spin up a from scratch architecture only when the use case truly needs it.
- Own the full lifecycle. Prototype in notebooks, productionize via Python APIs or lightweight microservices, and wire up offline scoring, real-time inference, and monitoring.
- Make it self-serve. Wrap models in simple endpoints, SDKs, or SQL functions so analysts and engineers can self select the magic without a helpdesk ticket.
- Instrument & iterate. Track performance drift, cost, and business lift; retrain or retire ruthlessly based on evidence.
- Teach the village. Run demos, share code snippets, and mentor teammates on pragmatic ML patterns that survive first contact with customers.
- End to end ML product ownership-from prototype notebook to cloud native service
- Fluency in Python with libraries such as scikitlearn, PyTorch, TensorFlow, XGBoost, LightGBM
- Experience choosing and finetuning foundation/LLM or diffusion models when they're the quickest path to value
- Comfort with feature stores, vector databases, and MLOps stacks (Airflow/Prefect, MLflow, Kubeflow, SageMaker, Vertex, or equivalents)
- Both batch and low latency serving patterns (REST, gRPC, or streaming)
- SQL that hunts for signal in messy data and A/B results
- Solid grounding in statistics and experimental design, plus the storytelling chops to explain lift to non-data partners
- Version control, CI/CD, and a bias toward shipping thin vertical slices over monoliths
- Think "impact > model elegance." You pick the simplest approach that moves the KPI.
- Prototype loudly. You'd rather show a working demo than a 40page deck.
- Stay humble. If a spreadsheet baseline wins, you celebrate-and then raise the bar.
- Translate effortlessly. You can chat GPU kernels at noon and revenue funnels at 12:05.
- Love ambiguity. Blank whiteboards signal possibility, not paralysis.
- Velocity. Your models meet customers in weeks, not quarters.
- Tool freedom. Choose the stack that fits the problem-no six month procurement saga.
- Breadth. Projects jump from ad-tech optimization to identity resolution to GenAIpowered personalization.
- Colleagues who get it. Sharp minds, low egos, and a shared hunger for measurable business results.
- Global flexibility. Work where you think best-our culture is built for distributed teams.
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