Data Scientist AI/ML

Role Overview The company helps clients become data-driven by applying data + technology to create real-time,

actionable visibility into business performance and to build lasting analytics capability. This role will help expand delivery capacity for pragmatic, production-grade analytics, ML, and GenAI solutions, so client teams can move from “interesting insights” to deployed capabilities with measurable value.

What you’ll do • Translate client business questions into analytic approaches (metrics, segments, hypotheses), then deliver recommendations leaders can act on. • Perform data exploration to identify drivers, anomalies, and performance constraints; communicate findings clearly to non-technical stakeholders. • Design, run, and analyse experiments (A/B tests) and/or apply causal inference where experimentation is constrained. • Build predictive models (e.g., risk/propensity/forecasting) with sensible baselines, validation, and error analysis, document tradeoffs. • Build LLM/GenAI applications (prompt workflows, tool use, structured outputs) and implement RAG when retrieval improves accuracy. • Create evaluation approaches for LLM systems (test sets, rubrics, automated checks, and human review loops) and iterate based on evidence. • Implement guardrails: PII handling, secure prompting patterns, input/output validation, rate limiting, and fallback behaviors. • Package models and GenAI components into clean services (APIs/batch jobs), with basic CI/CD awareness and production readiness. • Improve/extend data pipelines and feature reliability (freshness, data quality checks, lineage-friendly design). • Contribute to value measurement: define success metrics, track outcomes, and help clients understand what moved and why (the company emphasizes value measurement as part of delivery). • Work within a structured engagement approach (Discover Review → Implement → Value → Evolve), producing lightweight deliverables clients can run with.

Role Expectations • Strong fundamentals in SQL + Python for analysis, transformation, and reproducible work (notebook + production-friendly code). • Experience delivering at least one end-to-end analytics or ML project (problem framing → data → model/analysis → decision or deployment). • Working knowledge of experimentation (A/B testing) and common pitfalls; comfort explaining statistical reasoning simply. • Exposure to LLM application development (prompting, a small RAG prototype, or integrating an LLM into a workflow). • ML engineering fundamentals: Git, code reviews, basic testing mindset, and ability to build/maintain a simple API or batch pipeline. • Data quality mindset: you proactively validate inputs/outputs and can implement basic checks (freshness, nulls, ranges, duplicates). • Responsible AI awareness: privacy/PII, security basics, bias considerations, and safe deployment practices. • Communication skills: you can present to mixed audiences and write clear docs (assumptions, approach, results, recommendations). • Comfort in a consulting-style environment: shifting context, collaborating with client teams, and managing expectations professionally.

Nice To Haves • Familiarity with cloud and modern data stacks used in consulting projects (warehouses and orchestration). • Experience with BI/analytics enablement (semantic layers, dashboards, metric definitions, stakeholder training). • Familiarity with vector search tooling and embedding workflows. • Exposure to MLOps tooling (model tracking, monitoring, alerting) and basic incident/debugging habits. • Any experience working with executives or operational leaders on decision-making and value measurement. • Domain depth in a client-relevant industry: [finance/telecom/retail/public sector/health/etc.] • Cloud experience (Azure/AWS/GCP) with any Data Science / AI / Data Engineering certifications. • Databricks and/or Snowflake experience

Role Overview The company helps clients become data-driven by applying data + technology to create real-time,

actionable visibility into business performance and to build lasting analytics capability. This role will help expand delivery capacity for pragmatic, production-grade analytics, ML, and GenAI solutions, so client teams can move from “interesting insights” to deployed capabilities with measurable value.

What you’ll do • Translate client business questions into analytic approaches (metrics, segments, hypotheses), then deliver recommendations leaders can act on. • Perform data exploration to identify drivers, anomalies, and perfor