Data Scientist

• Design, evaluate, and deploy RAG pipelines, agentic systems, and chat interfaces, including advanced retrieval methods and modular designs.

• Conduct LLM red-teaming, bias detection, and alignment testing; build automated evaluation frameworks and develop measurement/feedback loops.

• GenAI engineering in the Azure ecosystem: integrate and test LLMs using OpenAI APIs, Azure AI Services, Azure AI Search/Cognitive Search, Azure ML, Databricks, and (where applicable) AKS/Kubernetes.

• Develop and implement ML models (traditional + GenAI) and design/execute experiments to validate, optimize, and scale solutions.

• Contribute to fine-tuning and pre-training pipelines for domain use cases; use synthetic data generation and creative data sourcing to improve modeling outcomes (including sparse/rare-event problems).

Data acquisition & domain understanding

• Identify, evaluate, and obtain access to internal and external data sources with minimal guidance.

• Partner with subject matter experts to understand data definitions, quality, lineage, and business context.

• Document datasets using standard templates (metadata, assumptions, refresh cadence, known issues) and present findings to stakeholders.

Data engineering & platform delivery

• Design, build, and maintain robust ETL/ELT pipelines to extract, transform, and stage data for AI/ML and BI consumption.

• Create and manage tables, schemas, and databases; implement data models that support analytics and machine learning use cases.

• Productionize pipelines and datasets with monitoring, alerting, data quality checks, and performance tuning.

• Collaborate with internal/external engineers and data scientists to integrate data science solutions into production systems.

Analytics, measurement & reporting

• Deliver ad-hoc analysis, business analytics, and reporting for projects of moderate scope/complexity.

• Define metrics and acquire data to measure solution effectiveness; communicate results and recommendations to peers and stakeholders.

• Build dashboards and reporting assets (e.g., Tableau/Power BI/Qlik) to support decision-making.

Process design & operational excellence

• Help design scalable operating processes to implement analytics/AI insights, including:

• Clear roles and responsibilities across partners

• Defined workflows, system/data flows, and contingencies

• Checks-and-balances (validation, QA, approvals)

• KPI tracking and closed-loop learning to continuously improve outcomes

Communication & mentorship

• Partner with Data Engineers, ML Engineers, BI, IT, and business leaders; participate in code/model reviews, documentation, agile delivery, and mentorship.

• Communicate moderately complex technical and analytical topics to senior team members and business partners.

• Build a strong internal network and leverage senior peers to accelerate delivery.

Required Qualifications:

Education & experience

• Bachelor’s degree in Computer Science, Engineering, Math, or equivalent practical experience.

• 2+ years of relevant experience in data engineering, analytics engineering, or BI/analytics roles supporting AI/ML or advanced analytics.

• Advanced degree (MS/PhD) in a quantitative field or equivalent depth through impactful work; publications or research contributions are a plus.

Technical skills

• Strong programming skills in Python; advanced SQL proficiency; Java experience is asset.

• Advanced experience with data transformation and modeling for analytics/BI and ML feature readiness.

• Strong understanding of relational databases and data modeling concepts.

• Working knowledge of distributed computing concepts/tools.

• Advanced experience with data visualization tools

Analytics/ML foundations

• Ability to explore and mine large structured and unstructured datasets using a systematic approach.

• Familiarity with statistics and common analytical techniques (e.g., regression, clustering, PCA, decision trees, survival analysis).

• Basic understanding of machine learning algorithms and familiarity with common AI/ML toolkits.

• Hands-on experience with RAG, embeddings, semantic search, and vector databases (e.g., FAISS, MongoDB, Neo4j) and/or graph-based analytics.

• Knowledge of GenAI frameworks such as LangChain, Semantic Kernel, and agent frameworks (e.g., Autogen / LangGraph / CrewAI-style tools).

(Optional) Include with your application

• 1-2 examples of work (GitHub, write-ups, papers, demos, etc.) showing:

• something you built end-to-end,

• how you evaluated quality and failure modes,

• and how you iterated based on evidence and constraints.

When you join our team:

• We’ll empower you to learn and grow the career you want.

• We’ll recognize and support you in a flexible environment where well-being and inclusion are more than just words.

• As part of our global team, we’ll support you in shaping the future you want to see.

#LI-Hybrid