Data Science Lead - R01570082

On-siteposted 2w ago

Skills required

Data science Product management LLMs Python RAG TensorFlow PyTorch Azure Statistics Kubeflow Prompt engineering Machine learning AI

About this role

Data Science Lead

Job requirements

With at least 8 years of experience in technical product management, engineering management, or similar roles leading technical teams in AI/ML or data-driven product development Key Responsibilities:

Set strategic priorities and determine team focus across AI Enablement and AI Experiments tracks, ensuring measurable progress toward organizational goals

Serve as the primary liaison with internal business teams to understand workflows, gather requirements, and translate business pain points into actionable technical work

Collaborate with product teams to align exploration and experimentation efforts with broader product direction

Lead the team’s operating rhythm, including stand-ups, demos, planning sessions, and progress readouts to leadership and stakeholders

Allocate resources across workstreams, moving team members based on shifting priorities to maximize impact and efficiency

Evaluate and shut down experiments or projects that are not delivering results, reprioritizing efforts swiftly and effectively

Guide the team’s technology roadmap by making decisions on model selection, infrastructure, build-vs-buy tradeoffs, and adoption of new tools

Define and evolve AI governance and compliance practices, establishing guardrails for responsible AI use, data handling, and decision explainability

Manage and optimize AI infrastructure spend, tracking LLM costs, token usage patterns, and vendor contracts to ensure cost-effective operations

Required Skills:

Advanced proficiency in Python for code review, scripting, and prototyping

Strong understanding of LLM-based systems, including retrieval-augmented generation pipelines

Experience with ML frameworks such as TensorFlow, PyTorch, and Sci-Kit Learn

Hands-on experience with Azure cloud infrastructure for deploying, monitoring, and scaling AI workloads

Expertise in statistical analysis and computing, including hypothesis testing, t-test, z-test, and regression techniques

Proficiency in forecasting techniques including exponential smoothing, ARIMA, and ARIMAX

Knowledge of classification algorithms such as decision trees and SVM

Familiarity with tools like KubeFlow and BentoML for ML lifecycle management

Understanding of probabilistic graph models and advanced distance metrics (Hamming, Euclidean, Manhattan)

Preferred Skills:

Experience with agent orchestration patterns for multi-step AI workflows

Expertise in prompt engineering to optimize output quality in LLM-based systems

Proficiency with Great Expectations and Evidently AI for data validation and monitoring

Experience defining AI governance frameworks for compliance and responsible data handling

Desired Qualifications:

Bachelor's degree in Computer Science, Data Science, Statistics, Information Technology, or a closely related discipline

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