Manager, Data Science

Data & Analytics | Remote

Reports To Director, Data Ops & Analytics

Location

Remote

About the Role

A growing organization is building out its data science function and looking for a Manager, Data Science who is as comfortable building models as they are developing the analysts beneath them. This is a hands-on leadership role: you will define and drive the analytics roadmap, build and ship predictive models into production, and mentor a small team of two direct reports in a high-growth, fast-pivoting environment.

You will operate with significant autonomy, report to the Director of Data Ops & Analytics, and serve as the authoritative voice on data science methodology across the organization. A mature but evolving Snowflake environment (ODS fed from a CRM platform) is your primary data layer, with AWS as the cloud backbone and Snowflake Cortex as an emerging AI/NLP layer you will grow into.

The right person thrives in a smaller organization where scope is broad, priorities shift quickly, and the expectation is to dig in and figure it out.

What You'll Do

Predictive Modeling & Analytics

1. Build and deploy ML models into production that directly influence business decisions: lead scoring, conversion forecasting, marketing attribution, fraud detection, and capacity planning.
2. Run predictive analytics, prescriptive analytics, and anomaly detection against a near-real-time Snowflake ODS environment.
3. Leverage open search / Elasticsearch-style tooling for alerting and pattern detection.
4. Explore and extend the team's use of Snowflake Cortex (including Cortex Agents and Cortex Analyst) as the organization matures its AI/NLP layer.

Team Leadership & Mentorship

1. Manage and develop two direct reports (Senior Data Analyst and Data Analyst). This is the primary expectation of the role: you teach every model before you build it alone.
2. Raise the data science capability of the team through hands-on mentorship, paired work, and knowledge transfer.
3. Participate in hiring decisions as the team scales.

Data Governance & Standardization

1. Own metric definitions, calculations, and documentation across the organization. Establish one authoritative version of each KPI.
2. Serve as an active member of the Data Governance Council to resolve cross-functional metric conflicts between business units.
3. Enforce appropriate data access guardrails in collaboration with the Director.

Roadmap & Stakeholder Engagement

1. Own and drive the analytics roadmap. Expect roughly half of it to be defined on arrival; the other half is yours to shape.
2. Present strategic insights and recommendations to executive leadership monthly, with measurable business impact.
3. Partner with the Data Engineering team to prioritize data quality, ETL improvements, and infrastructure needs.

What You Bring

1. 7–12 years of data science or analytics experience with increasing responsibility.
2. At least 2 years in a role with direct mentorship or management of analysts or data scientists.
3. Demonstrated ability to build and ship predictive models (regression, classification, time series) that influenced real business strategy.
4. Strong stakeholder management skills and the ability to communicate technical findings to executive audiences.
5. Proven track record of working across multiple platforms and problem types. Breadth matters more than depth in a single domain.
6. Intellectually curious, self-directed, and comfortable operating in a dynamic environment where roadmap priorities evolve.

Technical Skills

1. Expert-level SQL and Python. R is a nice-to-have.
2. Snowflake: proficient as a cloud data warehouse and ODS. Familiarity with Snowflake Cortex (Cortex Analyst, Cortex Agents, Cortex Code / Streamlit) is a meaningful plus.
3. AWS: cloud services familiarity in the context of data pipelines and infrastructure.
4. Open search / Elasticsearch: experience in alerting, search-based analytics, or NLP-adjacent tooling.
5. CRM data modeling: sufficient understanding to navigate and interpret CRM-sourced data in Snowflake.
6. Familiarity with BI tooling (Qlik or similar).

Nice to Have

1. Experience with LLM integration, RAG architecture, or applied generative AI in a production environment.
2. Background in Streamlit for lightweight data app delivery.
3. Exposure to integration middleware in the context of data access governance.
4. Relevant certifications (AWS Certified Machine Learning, Google Professional Data Engineer, or equivalent).

Education

1. Bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative field required.
2. Master's degree preferred, or equivalent combination of education and demonstrated experience.

What Success Looks Like

1. 3+ predictive models shipped to production within the first 12 months that demonstrably influence business decisions.
2. 10–15% improvement in conversion rates, marketing ROI, or operational efficiency gains documented quarterly.
3. 90%+ of committed analytical projects delivered on time and within scope.
4. Standardized metric definitions adopted across business units, reducing cross-platform reporting discrepancies.

Why Join

1. Broad scope from day one: you own the data science roadmap, not a slice of someone else's.
2. Direct access to leadership: daily standups, monthly exec presentations, and a director who wants you to drive, not just execute.
3. A team that takes mentorship seriously.
4. A stable, growing business in a specialized niche with a strong operational data foundation ready for sophisticated modeling.
5. Recognized as a Best Place to Work.

Interested? Apply or reach out directly.

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