Data Analytics Consultant

We are a consulting and engineering firm that helps clients solve complex data, analytics, and technology challenges. We are growing our Data & Analytics practice and looking for a Data Analytics Consultant who thrives where business thinking meets technical execution.

The right candidate is as comfortable leading a client discovery session as they are building a data pipeline or analytical model. You translate ambiguous business problems into clear delivery requirements, execute across data engineering, business intelligence, and data science, and communicate results in ways that drive real decisions.

As a Data Analytics Consultant, you will be embedded on client engagements where you shape the analytical direction of a project, execute across the data stack, and translate technical findings into business value. You serve as a trusted advisor to client stakeholders while maintaining the hands‑on technical credibility to deliver.

• Lead client‑facing discovery sessions to understand business problems, define analytical scope, and align on delivery approach
• Translate ambiguous client needs into structured delivery requirements, project plans, and executable work streams
• Serve as a trusted advisor to client stakeholders on data strategy, analytical approach, and solution tradeoffs
• Own and lead analytical and modeling initiatives from problem definition through delivery with minimal oversight
• Execute hands‑on across data engineering, business intelligence, and data science disciplines as project needs demand
• Communicate results, insights, and recommendations clearly to both technical and executive audiences
• Design and interpret analyses involving ambiguous or noisy data; explain tradeoffs between accuracy, interpretability, and business impact
• Review teammates’ work for correctness, clarity, and best practices; mentor less experienced team members
• Contribute to project scoping, estimation, and delivery planning with project leadership
• Ask clarifying questions before diving in, flag blockers early, and proactively manage stakeholder expectations

• Experience in data analytics, data science, or a related consulting or delivery role
• Demonstrated ability to lead client‑facing engagements: discovery, scoping, road mapping, and requirements definition
• Proven ability to translate technical findings into executive‑ready narratives and recommendations
• Broad hands‑on execution capability across data engineering (pipelines, ELT/ETL), business intelligence (reporting, dashboards), and data science (modeling, experimentation)
• Independently frames, scopes, and leads analytical and modeling problems from ambiguous business questions
• Selects, trains, and evaluates models using appropriate validation techniques
• Strong grasp of statistical foundations: hypothesis testing, experimental design, regression, probability theory
• Applied experience with MLOps concepts: model serving, monitoring, and explainability
• Track record of managing multiple work streams and client relationships in a project‑based delivery environment
• Comfortable navigating ambiguous client briefs, working with limited information, and scoping work iteratively

• Version control:
  Git
• Notebooks & exploration:
  Jupyter

• Distributed processing:
  Spark, Py Spark
• Pipeline & MLOps tooling: MLflow, DVC

• Supervised and unsupervised learning; regression, classification, clustering
• Experimental design, A/B testing, and causal inference
• Applied NLP or LLM integration experience a plus
• Familiarity with deep learning concepts

• Hands‑on experience integrating LLMs into analytical workflows — prompt engineering, RAG, or AI‑assisted data exploration
• Built or contributed to a project using AI APIs (OpenAI, Anthropic, Hugging Face, etc.)
• Experience using AI‑assisted coding tools (Git Hub Copilot, Cursor, etc.) to accelerate analysis or pipeline development
• Portfolio, Git Hub repo, or write‑up demonstrating curiosity and initiative with AI tooling
• Awareness of responsible AI principles (bias, fairness, and explainability) especially in client‑facing contexts

• You walk into an ambiguous client situation and leave with a clear path forward
• You ask clarifying questions before diving in and flag blockers early
• You care about the business outcome, not just the model metric
• You communicate uncertainty honestly - not just the answer, but your confidence in it
• You build reproducible, well‑documented analyses that others can build on
• You leave codebases, teammates, and client relationships better than you found them