- Company Name
- Infrastructure Ontario
- Job Title
- Data Science Manager
- Job Description
-
**Job title**: Data Science Manager
**Role Summary**:
Strategic and hands‑on leader responsible for translating business challenges into analytics solutions, driving enterprise data foundations, and guiding cross‑functional teams on best practices in data science, quality, and governance.
**Expectations**:
- Report to Director of Data & Analytics.
- Serve as a trusted advisor to business domains, shaping analytics strategy and ensuring alignment with organizational goals.
- Deliver high‑quality analytical products while building reusable assets for future scaling.
- Mentor junior analysts and foster a vibrant analytics community.
**Key Responsibilities**:
- Partner with business units to define key problems, distill them into clear analytics requirements, and prioritize projects.
- Conduct exploratory data analysis, build predictive models (forecasting, classification, NLP when relevant), and translate insights into actionable recommendations.
- Design and develop end‑to‑end analytical pipelines, dashboards, and visualization products using modern BI tools.
- Create reusable templates, data pipelines, and modeling frameworks for efficient reuse across the enterprise.
- Lead data quality initiatives: define rules, profile datasets, identify issues, and collaborate with IT and business stakeholders on remediation and monitoring.
- Collaborate with data engineers and governance teams to specify requirements for a centralized analytics platform and contribute to metadata standards.
- Participate in AI governance discussions, evaluating feasibility and ethical implications of new use cases.
- Develop and deliver data literacy materials, enabling self‑service analytics across business lines.
- Coach and mentor junior staff, providing technical guidance and professional growth opportunities.
**Required Skills**:
- Strong analytical and statistical background with proficiency in Python, R, and SQL.
- Expertise in database design, data warehousing, and ETL/ELT processes.
- Experience with modeling techniques (forecasting, classification, NLP), model deployment, and performance monitoring.
- Familiarity with BI and visualization tools (e.g., Power BI, Tableau, Looker).
- Knowledge of data governance concepts, metadata management, and data quality frameworks.
- Excellent stakeholder communication, project prioritization, and solution‑design skills.
- Proven ability to mentor or coach junior data professionals.
**Required Education & Certifications**:
- Advanced degree (Master’s or higher) in Statistics, Applied Mathematics, Computer Science, Business Analytics, or a related field.
- Relevant industry certifications (e.g., Microsoft Certified: Data Scientist Associate, SAS Certified Data Scientist) are an asset but not mandatory.