AI & Analytics Innovation Senior Product Specialist
- 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-usin g 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-inclu ding requirements, code, architecture plans, and delivery roadmaps-for longer-term implementation teams.
A successful candidate would possess these skills:
- Ability to work independently and collaborate as part of a team
- Effective written and verbal communication skills
- Meticulous attention to detail and quality of work product
- Ability to build and sustain professional relationships
- Ability to lead projects or workstreams
- Ability to manage and prioritize multiple tasks in a fast-paced and dynamic environment
- Strong interpersonal skills and professional demeanor
- Ability to meet deadlines
- Ability to provide clear guidance to others
The Team
The AI & Analytics CoE acts as a rapid analytics development engine supporting Strategy & Transactions engagements by building prototypes and MVP analytics solutions that inform critical business decisions. Qualifications Required:
- Bachelor's degree and 2+ years of experience in analytics, data science, or consulting.
- 2+ years experience using Python for analytics and modeling (e.g., Pandas, NumPy, scikit-learn; stats models for inference/fore casting; P lotly/Matplot lib for visualization or similar libraries).
- 2+ years experience using applied modeling: e.g., regression/cla ssification, tree-based models (random forest, GBMs), clustering/seg mentation, and time-series forecasting basics.
- 2+ years experience using Structured Query Language ( SQL) for data extraction and transformation (CTEs, window functions, query readability).
- 2+ years experience with Git and collaborative workflows (PRs, code review, reproducibilit y).
- 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 25%, on average, based on the work you do and the clients and industries/sectors you serve.
- Limited immigration sponsorship may be available
- Experience with cloud data platforms/ware houses (e.g., Snowflake, BigQuery, Redshift) and/or Spark (e.g., D atabricks/EMR) .
- Experience communicating analytical insights through dashboards or visualizations using 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 i nterpretation, bias/variance intuition) and evaluation discipline (train/test splits, cross-validati on, leakage awareness, appropriate metrics).
- Familiarity with dbt, Airflow, or equivalents and data quality tooling/practi ces.
- Familiarity with financial concepts and interest in relevant offerings (Strategy, M&A, S ustainability, etc.).
- Experience collaborating across teams, coaching junior staff, and influencing others in a project-based environment.
- Degree in Computer Science, Statistics, Mathematics, Economics, Finance, Engineering, Natural Sciences, or another quantitative field.
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