Head of Data Science (Remote)

Jobgether

7d•Remote

About The Position

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Head of Data Science (Remote). In this role, you will be the technical and strategic owner responsible for product experimentation and evaluation within our partner's operations. Your work will directly impact the accuracy and efficiency of digital ordering systems, driving insights through machine learning to enhance merchant success. You will collaborate across teams to transform innovative ideas into valuable experiments and product enhancements that deliver measurable results in a dynamic environment. This role combines technical expertise with strategic vision to foster growth and optimization.

Requirements

8â12+ years of experience in data science, machine learning, or applied experimentation roles.
Demonstrated expertise in product experimentation and A/B testing, including design, execution, and statistical evaluation.
Strong background in machine learning, statistical modeling, and causal inference applied to real-world products.
Experience building and evaluating predictive models, classifiers, or ranking systems in production environments.
Proven ability to operate in both startup-style experimentation and scaled product ecosystems.
Experience leading teams, setting technical direction, and delivering cross-functional impact.
Excellent coding skills in Python (or similar), strong SQL, and experience building data pipelines or ML systems.
Ability to connect technical findings to product and business outcomes.
Strong communication skills with technical and non-technical stakeholders.

Responsibilities

Own end-to-end product experimentation: hypothesis generation, metric definition, experimental design (A/B, multivariate, sequential testing), analysis, and executive-level interpretation.
Design and maintain ML-powered evaluation frameworks for product changes, automation quality, and system reliability.
Build and deploy predictive models, classifiers, and ranking systems that power experimentation, personalization, and product optimization.
Partner with product and engineering to test new features, workflows, and ML models through controlled experiments.
Lead offline and online model evaluation, comparing baselines, candidate models, and product variants.
Use causal inference and quasi-experimental methods when randomized experiments are not feasible.
Develop experiment pipelines and instrumentation: logging, dashboards, monitoring, and automated analysis.
Perform failure-mode and error analysis to guide product iteration and model improvement.
Translate experiment outcomes into clear product decisions, influencing roadmap prioritization and system design.
Drive experimentation at scale in a fast-moving environment, balancing speed, rigor, and business impact.
Lead and mentor data scientists and analysts, setting standards for experimentation, modeling, and evaluation.