AI & Analytics Innovation Senior Consultant
- Frame high-value business questions, define KPIs, and develop hypotheses that drive actionable analysis for deal teams and clients.
- Assess data sources, evaluate quality and feasibility, and define minimum viable datasets to support rapid analytics delivery.
- Rapidly build proof-of-value prototypes and MVPs-including dashboards, predictive models, segmentation analyses, and AI-enabled analytics-using real client data.
- Apply reusable frameworks, templates, and accelerators to compress delivery timelines and reduce project risk.
- Establish lightweight data governance, metric definitions, and documentation to ensure solutions are sustainable beyond initial delivery.
- Prepare handoff packages-including requirements, code, architecture plans, and delivery roadmaps-for longer-term implementation teams.
- 2-4 years of professional experience in analytics, data science, consulting, or a related field.
- Bachelor's degree.
- 2+ years experience with Python for analytics and modeling (e.g., Pandas, NumPy, scikit-learn; stats models for inference/forecasting; plotly/matplotlib for visualization or similar libraries).
- 2+ years experience with applied modeling: e.g., regression/classification, tree-based models (random forest, GBMs), clustering/segmentation, and time-series forecasting basics.
- 2+ years experience with SQL for data extraction and transformation (CTEs, window functions, query readability).
- 2+ years experience with Git and collaborative workflows (PRs, code review, reproducibility).
- Experience using LLMs and AI-assisted development in day-to-day work: coding with AI assistants, using APIs for data collection, and integrating AI into analytical workflows.
- Ability to travel up to 25% as needed.
- Live within commuting distance to one of Deloitte's consulting offices.
- Limited immigration sponsorship may be available.
- Experience with cloud data platforms/warehouses (e.g., Snowflake, BigQuery, Redshift) and/or Spark (e.g., Databricks/EMR).
- Experience communicating analytical insights through dashboards or visualizations (e.g., Tableau, Power BI, or Python visualization libraries).
- Experience producing reliable reporting datasets: basic pipeline thinking, refresh schedules, data quality validation.
- Strong foundation in statistics (confidence intervals, regression interpretation, bias/variance intuition) and evaluation discipline (train/test splits, cross-validation, leakage awareness, appropriate metrics).
- Familiarity with dbt, Airflow, or equivalents and data quality tooling/practices.
- Familiarity with financial concepts and interest in relevant offerings (Strategy, M&A, Sustainability, etc.).
- Experience collaborating across teams, coaching junior staff, and influencing others in a project-based environment.
- Degree in a quantitative, scientific, business, or related field (e.g., Computer Science, Statistics, Mathematics, Economics, Finance, Engineering, Natural Sciences).
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