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.About the RoleAs 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.ResponsibilitiesLead client-facing discovery sessions to understand business problems, define analytical scope, and align on delivery approachTranslate ambiguous client needs into structured delivery requirements, project plans, and executable workstreamsServe as a trusted advisor to client stakeholders on data strategy, analytical approach, and solution tradeoffsOwn and lead analytical and modeling initiatives from problem definition through delivery with minimal oversightExecute hands-on across data engineering, business intelligence, and data science disciplines as project needs demandCommunicate results, insights, and recommendations clearly to both technical and executive audiencesDesign and interpret analyses involving ambiguous or noisy data; explain tradeoffs between accuracy, interpretability, and business impactReview teammates’ work for correctness, clarity, and best practices; mentor less experienced team membersContribute to project scoping, estimation, and delivery planning with project leadershipAsk clarifying questions before diving in, flag blockers early, and proactively manage stakeholder expectationsSkills & ExperienceExperience in data analytics, data science, or a related consulting or delivery roleDemonstrated ability to lead client-facing engagements: discovery, scoping, roadmapping, and requirements definitionProven ability to translate technical findings into executive-ready narratives and recommendationsBroad 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 questionsSelects, trains, and evaluates models using appropriate validation techniquesStrong grasp of statistical foundations: hypothesis testing, experimental design, regression, probability theoryApplied experience with MLOps concepts: model serving, monitoring, and explainabilityTrack record of managing multiple workstreams and client relationships in a project-based delivery environmentComfortable navigating ambiguous client briefs, working with limited information, and scoping work iterativelyTools & TechnologiesCORE STACKLanguages: Python (primary), SQLVersion control: GitNotebooks & exploration: JupyterVisualization & reporting: Power BI, TableauML libraries: Scikit-learn, Pandas, NumPyPLATFORMS & CLOUDData platforms: Databricks, Snowflake, Microsoft FabricCloud & ML services: Azure Machine Learning Studio, AWS SageMakerDistributed processing: Spark, PySparkPipeline & MLOps tooling: MLflow, DVCML APPROACHESSupervised and unsupervised learning; regression, classification, clusteringExperimental design, A/B testing, and causal inferenceApplied NLP or LLM integration experience a plusFamiliarity with deep learning conceptsNice to HaveAI TOOLING & APPLIED PROJECTSThe analytics landscape is shifting fast. We value candidates who have rolled up their sleeves with AI tools — not theoretical familiarity, but things actually built or experimented with:Hands-on experience integrating LLMs into analytical workflows — prompt engineering, RAG, or AI-assisted data explorationBuilt or contributed to a project using AI APIs (OpenAI, Anthropic, Hugging Face, etc.)Experience using AI-assisted coding tools (GitHub Copilot, Cursor, etc.) to accelerate analysis or pipeline developmentPortfolio, GitHub repo, or write-up demonstrating curiosity and initiative with AI toolingAwareness of responsible AI principles (bias, fairness, and explainability) especially in client-facing contextsWhat Great Looks LikeYou walk into an ambiguous client situation and leave with a clear path forwardYou ask clarifying questions before diving in and flag blockers earlyYou care about the business outcome, not just the model metricYou communicate uncertainty honestly - not just the answer, but your confidence in itYou build reproducible, well-documented analyses that others can build onYou leave codebases, teammates, and client relationships better than you found them