Technical Product Manager, Data Science

Benefits & Perks

• Everything you need to work remotely
• Unlimited PTO
• Medical/dental/vision insurance
• 401k through Charles Schwab 
• Flexible Spending Account, Limited FSA, and Health Savings Account- with an eligible health care package. 
• Company-paid short-term and long-term disability plus basic life insurance.
• Family-friendly maternity and paternity leave
• Employee assistance program (EAP) via Claremont. Get free short-term counseling for mental health, free + discounted legal consultations, free financial consultations, access to work/life consultants, and more!
• PerkSpot discount program. PerkSpot offers exclusive discounts to 900+ merchants nationwide, and has exclusive discounts up to 60% on hotels worldwide.
• Paid time off to do volunteer work in your community.
• Access to the Wellness Coach app for you and 5 family members

Responsibilities

• Product Strategy & Vision: Define and champion the product vision, strategy, and roadmap for data science initiatives, aligning with overall company objectives and market opportunities.
• Roadmap & Prioritization: Develop and maintain a prioritized product backlog based on business value, technical feasibility, user feedback, and strategic goals. Make the tough trade-off decisions necessary to ensure we efficiently deliver value to users and our business.
• Requirements Definition: Collaborate closely with data scientists, engineers, designers, and stakeholders to gather requirements, define use cases, and translate them into clear, detailed technical specifications and user stories.
• Cross-functional Collaboration: Act as the primary liaison between the Data Science team and other departments (Engineering, Marketing, Sales, Operations, etc.) to ensure alignment and successful product development and launch.
• Technical Understanding: Develop a deep understanding of our data infrastructure, machine learning models, algorithms, and analytical techniques to effectively guide product development and communicate technical concepts to non-technical audiences.
• Product Lifecycle Management: Oversee the entire product lifecycle from ideation and discovery through development, launch, iteration, and end-of-life.
• Market & User Research: Conduct market research, competitive analysis, and user research to identify unmet needs, validate hypotheses, and inform product decisions.
• Go-to-Market: Partner with marketing and sales teams to define go-to-market strategies, positioning, and messaging for new data products and features.
• Performance Monitoring: Define key performance indicators (KPIs) and success metrics for data science products; monitor performance, analyze results, and drive continuous improvement.
• Stakeholder Management: Communicate product plans, progress, and insights effectively to stakeholders at all levels, including executive leadership.

Qualifications (Required)

• Bachelor's degree or equivalent professional experience in Computer Science, Engineering, Statistics, Mathematics, or a related technical field.
• 3+ years of experience in technical product management, preferably working with data science, machine learning, or AI-powered products.
• Proven ability to define product strategy, develop roadmaps, and manage a product backlog.
• Strong understanding of the data science lifecycle, including data collection, cleaning, modeling, validation, and deployment.
• Familiarity with common machine learning algorithms, statistical concepts, and data analysis techniques.
• Experience working closely with engineering and data science teams in an Agile/Scrum environment.
• Excellent analytical and problem-solving skills, with a data-driven approach to decision-making.
• Exceptional communication, presentation, and interpersonal skills, with the ability to influence and collaborate effectively across teams and levels.
• Demonstrated ability to translate complex technical concepts into clear business value.

Qualifications (Preferred)

• Master's or PhD degree in a relevant technical field.
• Hands-on experience with data science tools and platforms (e.g., Python, R, SQL, Spark, TensorFlow, PyTorch, cloud ML platforms like AWS SageMaker, Google AI Platform, Azure ML).
• Experience with A/B testing, experimentation frameworks, and data visualization tools (e.g. Tableau, Power BI).
• Experience in fintech, consumer finance, and/or consumer marketing.
• Experience managing platform products or APIs.