Hire Data Science Product Managers
Connect with Data Science Product Managers from Latin America. Strong in aligning predictive models to business outcomes with full setup in 21 days.
Hire Remote Data Science Product Managers
Matías is a data specialist streamlining systems through clean and efficient models.
- Data Warehousing
- ETL Processes
- Trend Reporting
- Dashboards
- Real-Time Data
Alejandra is a data expert helping teams make better decisions through deep analysis.
- Data Reporting
- Data Governance
- Visualization Tools
- Process Optimization
- Trend Monitoring
Pablo is a skilled data analyst known for insightful analysis and exceptional problem-solving.
- SQL
- Data Visualization
- Power BI
- Excel
- A/B Testing
Gabriela is a Data professional translating analytics into business opportunities.
- Data Analysis
- Business Intelligence
- SQL
- Reporting
- Data Visualization
Tomás is a skilled data analyst with a decade of experience, excelling in insightful analysis.
- Excel
- Data Visualization
- Power BI
- A/B Testing
- SQL
"Over the course of 2024, we successfully hired 9 exceptional team members through Lupa, spanning mid-level to senior roles. The quality of talent has been outstanding, and we’ve been able to achieve payroll cost savings while bringing great professionals onto our team. We're very happy with the consultation and attention they've provided us."
“We needed to scale a new team quickly - with top talent. Lupa helped us build a great process, delivered great candidates quickly, and had impeccable service”
“With Lupa, we rebuilt our entire tech team in less than a month. We’re spending half as much on talent. Ten out of ten”
Data Science Product Managers Skills
AI Roadmapping
Define features and delivery plans for data-driven products.
Model Lifecycle Management
Oversee development, testing, and retraining cycles.
Stakeholder Alignment
Translate model outputs into business impact.
Experiment Design
Structure A/B and multivariate tests for validation.
Data Acquisition Planning
Identify and prioritize key data sources.
Ethical AI Oversight
Monitor bias, transparency, and compliance risks.
Data Science Product Managers Soft Skills
Strategic Thinking
Connect model output to business outcomes and goals.
Prioritization
Balance model accuracy with delivery timelines.
Stakeholder Alignment
Facilitate shared understanding across teams.
Technical Fluency
Understand ML methods to guide planning and delivery.
Empathy
Consider user needs when productizing AI insights.
Communication
Translate data science impact to business language.
How to Hire Data Science Product Managers with Lupa
Align data science capabilities with strategic product goals. Partner via our Tech Recruiting Agency, grow fast with Remote Staffing Services, or embed recruitment with our RPO offering.
Together, we'll create a precise hiring plan, defining your ideal candidate profile, team needs, compensation and cultural fit.
Our tech-enabled search scans thousands of candidates across LatAm, both active and passive. We leverage advanced tools and regional expertise to build a comprehensive talent pool.
We carefully assess 30+ candidates with proven track records. Our rigorous evaluation ensures each professional brings relevant experience from industry-leading companies, aligned to your needs.
Receive a curated selection of 3-4 top candidates with comprehensive profiles. Each includes proven background, key achievements, and expectations—enabling informed hiring decisions.
Top candidates ready for your assessment. We handle interview logistics and feedback collection—ensuring smooth evaluation. Not fully convinced? We iterate until you find the perfect fit.
We manage contracting, onboarding, and payment to your team seamlessly. Our partnership extends beyond hiring—providing retention support and strategic guidance for the long-term growth of your LatAm team.
How to Write an Effective Job Post to Hire Data Science Product Managers
Recommended Titles
- AI Product Manager
- Data Product Manager
- ML Product Owner
- Analytics Product Manager
- Data Platform PM
- Tech Product Manager – Data Science
Role Overview
- Tech Stack: Familiar with Python, ML platforms, SQL, and product analytics tools.
- Project Scope: Bridge data science teams with business needs, prioritizing ML product delivery.
- Team Size: Work with 5–8 people across ML, engineering, and product functions.
Role Requirements
- Years of Experience: 3+ years in product management with a strong analytics foundation.
- Core Skills: ML use case scoping, experimentation frameworks, and metric ownership.
- Must-Have Technologies: Python (reading level), SQL, Airflow, Mixpanel, Jira.
Role Benefits
- Salary Range: $110,000 – $170,000 depending on ML domain exposure.
- Remote Options: Fully remote, with sync hours for cross-functional collaboration.
- Growth Opportunities: Own strategic AI initiatives with measurable product impact.
Do
- Emphasize cross-functional leadership and data fluency
- Mention ability to translate models into product outcomes
- Include stakeholder communication and data prioritization
- Highlight growth in AI/ML-powered product delivery
- Use analytical and product-strategic language
Don't
- Don’t use PM templates that ignore data fluency
- Avoid skipping AI/ML context or experimentation cycles
- Don’t post without stakeholder and cross-functional clarity
- Refrain from vague product goals like “optimize data use”
- Don’t overlook prioritization of model outcomes
Top Data Science Product Manager Interview Questions
How to assess Data Science Product Manager skills
How do you scope a data science feature?
Look for cross-functional collaboration, defining success metrics, and assessing data readiness or model complexity.
How do you communicate data science outcomes to executives?
Expect simplified storytelling, confidence intervals, trade-offs, and linking insights to business value.
What’s your process for prioritizing model improvements?
Look for alignment with business impact, error analysis, feedback loops, and lifecycle cost-benefit evaluation.
Describe your collaboration with data scientists and engineers.
They should mention shared documentation, sprint planning, pipeline tracking, and handling research vs. production gaps.
How do you define and track success for ML features?
Expect business KPIs, technical metrics (precision, recall), and engagement/retention lift or cost savings.
How do you handle scope changes caused by model limitations?
Look for impact mapping, stakeholder negotiation, and phased delivery strategies.
Describe a situation where your product required unexpected data labeling.
Expect adjustment of timelines, sourcing of annotation resources, and iteration of training cycles.
How do you validate if a data science feature is business-ready?
Expect statistical performance checks, user validation loops, and staged rollouts.
What’s your strategy when model output doesn’t align with user expectations?
Expect UX review, communication planning, and user education via in-product transparency.
How do you prioritize experimentation vs. shipping production ML features?
Expect risk frameworks, resource allocation trade-offs, and business impact scoping.
Tell me about a time you aligned scientists and engineers around a roadmap.
Expect planning sessions, common metrics, and boundary-setting between discovery and delivery.
Describe how you handle prioritization when experimentation outpaces product goals.
Expect trade-off management, stakeholder mediation, and clear backlog structure.
What’s your process when data science teams encounter ambiguity?
Expect iteration planning, defining MVP hypotheses, and business context translation.
How do you navigate business pressure to overpromise on AI capabilities?
Expect expectation-setting, use of use-case guardrails, and risk framing.
Have you managed misalignment between modeling outcomes and business KPIs?
Expect course correction, stakeholder engagement, and clear reframing.
- Inability to bridge data science and business impact
- Fails to set measurable goals for ML projects
- Weak understanding of model lifecycle and drift
- Prioritizes flashy AI features over user value
- Lack of alignment with data engineering timelines
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End-to-end remote talent solutions, from recruitment to payroll. Country-compliant throughout LatAm.
Recruiting
Our recruiting team delivers pre-vetted candidates within a week. Not the perfect match? We iterate until you're satisfied. You control hiring and contracts, while we provide guidance.
Staffing
Our recruiters deliver pre-vetted remote talent in a week. You select the perfect candidate, we manage onboarding, contracts, and ongoing payroll seamlessly.
RPO
Our RPO services deliver flexible talent solutions. From targeted support to full-cycle recruitment, we adapt and scale to meet your hiring goals while you focus on strategic growth.
