Senior Manager, Product Data Science & Experimentation

About the role

Data roles turn raw information into decisions. Analysts query databases and build dashboards. Data scientists build models that predict outcomes. Data engineers build the pipelines that move and store data. All three work closely with business teams and need a mix of statistics, coding, and communication. Nearly every modern company runs on data teams, from startups to banks. A strong portfolio of past analyses matters more than degrees in many hiring decisions.

Key facts

Requirements

A degree in a quantitative field is required. Extensive experience in data science or a similar quantitative role supports and influences a product organization. Expert level proficiency in Python and SQL is required, with deep, hands-on experience in statistical modeling, (quasi) experimentation, multi-arm bandit, and a wide range of machine learning techniques such as Regression, Classification, Clustering.

Demonstrated ability to define, implement, and operationalise crucial product and feature-level metrics from scratch is required. A proven track record of driving strategic impact through proactive collaboration is required, with the ability to lead technical discussions, drive product strategy, and communicate complex insights effectively to cross-functional partners.

Experience scaling analytics or data science capabilities through automated processes, self-service tools, or data products is required. Leadership in critical thinking, evidenced by analysis of facts, evidence, observations, and arguments to form judgments using rational, skeptical, and unbiased evaluation, is required.

Outstanding leadership skills, including mentoring, coaching, and developing teams of analysts or data scientists, are required. Exceptional collaboration and communication skills are required to engage, influence, and inspire cross-functional partners at all levels.

Proven ability to build strong relationships and drive outcomes across Product, Engineering, Data Platform and other central functions, often without direct authority, is required. A Bachelor's degree in a quantitative field is required.

Practical notes

You must hold a degree as specified in the requirements. The role is remote with engagement classified as #LI-remote. The position may involve travel. Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

Data science teams often use Python and SQL as core tools for analysis and modeling. Experimentation methods such as A/B tests and multi-arm bandit designs measure impact and guide decisions. Scalable analytical frameworks and self-service platforms help teams make data-driven decisions quickly. Causal inference and segmentation deepen understanding of customer behavior. Strong leadership and cross-functional communication are essential for analytics leaders in product organizations.

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.