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
Natalia excels in data analysis, delivering insights with precision and creativity.
- Power BI
- Data Visualization
- A/B Testing
- Excel
- SQL
Tomás is a skilled data analyst with a decade of experience, excelling in insightful analysis.
- Excel
- Data Visualization
- Power BI
- A/B Testing
- SQL
Patricio is a data operations expert simplifying processes for smarter decision-making.
- Data Visualization
- Database Management
- ETL Pipelines
- Statistical Modeling
- Python & SQL
Carlos excels in data science, blending innovation with precision. A master at turning data into insights.
- Statistics
- Data Cleaning
- Python
- Feature Engineering
- Machine Learning
Juliana, a skilled data scientist, excels in transforming data into actionable insights.
- Statistics
- Machine Learning
- Python
- Feature Engineering
- Data Cleaning
Rocío is a data professional helping teams act on metrics that matter most.
- Data Analysis
- Reporting
- Business Intelligence
- SQL
- Forecasting
Federico is a data thinker turning trends into opportunities for business growth.
- Data Analysis
- Insight Generation
- Reporting
- Business Intelligence
- Visualization Tools
"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”
Lupa's Proven Process
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.
Reviews
"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”
“We scaled our first tech team at record speed with Lupa. We couldn’t be happier with the service and the candidates we were sent.”
"Recruiting used to be a challenge, but Lupa transformed everything. Their professional, agile team delivers top-quality candidates, understands our needs, and provides exceptional personalized service. Highly recommended!"
“Lupa has become more than just a provider; it’s a true ally for Pirani in recruitment processes. The team is always available to support and deliver the best service. Additionally, I believe they offer highly competitive rates and service within the market.”
"Highly professional, patient with our changes, and always maintaining clear communication with candidates. We look forward to continuing to work with you on all our future roles."
“Lupa has been an exceptional partner this year, deeply committed to understanding our unique needs and staying flexible to support us. We're excited to continue our collaboration into 2025.”
"What I love about Lupa is their approach to sharing small, carefully selected batches of candidates. They focus on sending only the three most qualified individuals, which has already helped us successfully fill 7 roles.”
"We hired 2 of our key initial developers with Lupa. The consultation was very helpful, the candidates were great and the process has been super fluid. We're already planning to do our next batch of hiring with Lupa. 5 stars."
"Working with Lupa for LatAm hiring has been fantastic. They found us a highly skilled candidate at a better rate than our previous staffing company. The fit is perfect, and we’re excited to collaborate on more roles."
"We compared Lupa with another LatAm headhunter we found through Google, and Lupa delivered a far superior experience. Their consultative approach stood out, and the quality of their candidates was superior. I've hired through Lupa for both of my companies and look forward to building more of my LatAm team with their support."
“We’ve worked with Lupa on multiple roles, and they’ve delivered time and again. From sourcing an incredible Senior FullStack Developer to supporting our broader hiring needs, their team has been proactive, kind, and incredibly easy to work with. It really feels like we’ve gained a trusted partner in hiring.”
Working with Lupa was a great experience. We struggled to find software engineers with a specific skill set in the US, but Lupa helped us refine the role and articulate our needs. Their strategic approach made all the difference in finding the right person. Highly recommend!
Lupa goes beyond typical headhunters. They helped me craft the role, refine the interview process, and even navigate international payroll. I felt truly supported—and I’m thrilled with the person I hired. What stood out most was their responsiveness and the thoughtful, consultative approach they brought.
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.
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.
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
Build elite teams in record time, full setup in 21 days or less.
Book a Free ConsultationWhy We Stand Out From Other Recruiting Firms
From search to hire, our process is designed to secure the perfect talent for your team
Local Expertise
Tap into our knowledge of the LatAm market to secure the best talent at competitive, local rates. We know where to look, who to hire, and how to meet your needs precisely.
Direct Control
Retain complete control over your hiring process. With our strategic insights, you’ll know exactly where to find top talent, who to hire, and what to offer for a perfect match.
Seamless Compliance
We manage contracts, tax laws, and labor regulations, offering a worry-free recruitment experience tailored to your business needs, free of hidden costs and surprises.
Lupa will help you hire top talent in Latin America.
Book a Free Consultation
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
