About the position
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 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
Desired Skills:
- Strong SQL + Python 4 analysis
- transformation
- and reproducible work
- at least one end-to-end analytics
- or ML project
- (A/B testing) and common pitfalls
- LLM application development
