Job Description
Job Description:
Who We Are!
Pollfish, a Prodege, LLC company, is an online market research survey platform where data driven brands bring market research in-house for faster and smarter decision making. We have a proprietary network of 250M consumers/year which enables companies to connect with and understand real consumers worldwide in a fast, easy and cost-effective way.
Strategic Imperative:
The Sr. Data Scientist is responsible for producing actionable insights to drive decision-making across the business through rigorous analysis, experimentation, and business-focused data science. This role applies statistical methods, ML modeling, and behavioral analysis to quantify user behavior, optimize lifecycle marketing, evaluate product and monetization strategies, and inform business decisions. The role will closely collaborate with business stakeholders to translate business problems and data into solutions.
This position is centered on hands-on analytics, experimentation, and business impact. While the role will involve building predictive models, it is not focused on the deployment of live production models or ML Ops. The role is intended to incorporate AI agents to continuously improve and automate analytical processes
Primary Objectives:
Data Analysis & Insights Development: Drive data-informed progress across priority business areas by delivering rigorous analysis, actionable insights, and close partnership on optimization initiatives.
User Intelligence, Segmentation & Marketing Enablement: Develop and operationalize user-level insights, segmentation frameworks, and behavioral analysis that enable smarter targeting, personalization, and lifecycle engagement across growth and marketing programs.
Experimentation, Learning & Strategic Insight: Enable confident decision-making through statistically sound experimentation, structured insight generation, and systematic documentation of learnings to inform business planning, long-term strategy, and continuous improvement.
Qualifications - To perform this job successfully, an individual must be able to perform each job duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Detailed Job Duties: (typical monthly, weekly, daily tasks which support the primary objectives)
Analytical Insights & Modeling
Act as a strategic thought partner with stakeholders by enacting methodical business analysis and offering proactive diagnostics, observations, and recommendations.
Build and maintain customer segmentation, propensity, and scoring mechanisms for use in lifecycle marketing
Partner with marketing to devise and execute on policies and experimentation frameworks
Partner with Revenue Operations team to streamline pricing and rewards decisions through automated statistical frameworks.
Communicate findings clearly and document learnings to build reusable knowledge.
Provide technical guidance and mentorship to more junior team members.
Statistical Rigor
Define and evangelize best practices and frameworks for design and measurement to ensure statistical rigor
Partner on A/B tests across product, marketing, and monetization initiatives, owning statistical experimentation frameworks (Bayesian and frequentist) and tooling
Partner with teams on hypotheses, success metrics, and post-test actions.
Own the capture and scaling of learnings to improve future experimentation.
Data Foundations Support
Partner with Analytics Engineering to define canonical and semantic models and other business data requirements necessary to support a well functioning Data Science discipline.
Enable reliable lifecycle tracking and feature generation.
Expose behavioral learnings to ML engineers to inform feature and model development.
Partner with AI teams to translate workflows and recurring analytical patterns into agentic analytics systems that scale insight generation.
What does SUCCESS look like?
Success in this role is demonstrated by consistently translating data into clear, actionable decisions that improve product performance, lifecycle marketing effectiveness, and monetization outcomes. This is reflected in well-designed experiments and frameworks that drive confident business choices, analytical insights that meaningfully influence strategy, and strong partnerships with Analytics Engineering and Machine Learning through precise data definitions and feature guidance. Over time, success is evident in a growing body of documented learnings, trusted metrics, and a measurable impact on how the organization tests, learns, and optimizes.
The MUST Haves: (ex: job cannot be done without these skills, education, experience, certifications, licenses)
Bachelor’s or Master’s degree in Statistics, Economics, Data Science, Mathematics, or a related quantitative field.
Five or more (5+) years experience in data science roles.
Robust experience in statistical analysis and experimental design, including hypothesis testing and causal reasoning.
Understanding of frequentist and Bayesian statistical inference (e.g., PyMC) and demonstrated experience with A/B testing and experimentation frameworks.
Experience leading the analysis and optimization of lifecycle marketing performance across the user journey.
Fluency in SQL and ability to work with large, event-level datasets in data warehouse environments (e.g., Snowflake, BigQuery, Redshift).
Fluency in Python/R (pandas, matplotlib/seaborn, tidyverse, or similar) and robust experience with common ML packages (scikit-learn or similar).
Ability to develop effective data visualizations that clearly communicate insights to business stakeholders.
Strong communication skills with the ability to translate complex analysis into clear, actionable business insights.
Knowledge of agentic analytics workflows and best design practices.
High attention to data quality, metric definitions, and analytical rigor.
The Nice to Haves: (preferred additional skills, education, experience, certifications, licenses)
Experience in loyalty programs, performance marketing, or market research.
Deep learning experience
Experience building interactive dashboards in modern BI platforms.
Experience building agentic analytics workflows
Experience building data applications (e.g. Streamlit, Shiny)
Perks & Benefits:
An attractive salary package
Part of an innovative Global Tech Company
Private Health Insurance
Weekly Office Events - Catered Lunch and Breakfast
Stocked Kitchen
Company Outings & Quarterly Events
Hybrid Working
Meal Coupons - Monthly
LinkedIn Learning & Training Opportunities/Budget
Mental Health Benefits - Wellness Coach App Subscription
Great office location in the city center - Parking slots available
Gym Subscription - UP Fit
Quarterly Charitable Giving Allowance
Peer recognition Allowance