Data Science Manager (Remote)

Forbes Advisor•Wilmington, DE

18d•Remote

About The Position

Forbes Advisor is seeking a Data Science Manager to turn commercial problems into scalable Data Science products. The role partners with business and marketing teams to identify high-value opportunities, lead the development of reusable predictive models, and embed those models in business decision-making. It is a player-coach position: the manager leads and develops a growing global team of Data Scientists while staying technically involved in methodology, reviewing technical work, and contributing hands-on where useful. Working closely with the Director of Data Science, the manager will translate the Data Science strategy into scalable products and measurable business outcomes. The team focuses on customer intelligence, performance marketing, and commercial decision-making, including lifetime value, propensity modelling, customer value, forecasting, and marketing optimisation. The role is focused on applied work rather than research, with emphasis on production adoption, practical use, learning, and measurable commercial impact. The manager will collaborate with Engineering and ML Ops to move successful models into production, with suitable experimentation, monitoring, retraining, and feedback loops. They will build relationships across Marketing, Commercial, Analytics, Product, and Engineering, communicating modelling results as clear recommendations and helping the business decide what to do next. The job description also states that Forbes Advisor provides equal employment opportunities and prohibits discrimination and harassment across employment terms and conditions.

Requirements

Experience managing and developing Data Scientists while remaining comfortable working at a technical level.
Strong experience in predictive modelling, machine learning, and customer analytics.
Experience applying Data Science to commercial or performance marketing problems; ideally including LTV, propensity, customer value, or acquisition optimisation.
Strong Python and SQL skills.
Experience working in modern cloud-based Data Science environments.
Experience taking models beyond experimentation and working with Engineering or ML Ops teams to deploy and operate them in production.
Strong understanding of experimentation and model evaluation.
Excellent stakeholder management and communication skills, including translating complex technical work into clear recommendations for senior business audiences.
A product mindset and interest in building reusable capabilities rather than one-off models.
Strong commercial judgement and focus on measuring the real-world impact of Data Science.

Nice To Haves

Experience in performance marketing and optimisation across Google, Meta, or other major advertising platforms.
Experience with value-based bidding or conversion-value optimisation.
Direct collaboration with advertising platform Data Science or Marketing Science teams.
Experience in affiliate marketing, lead generation, or digital publishing.
Experience in financial services, insurance, health, or other regulated industries.
Experience with attribution and marketing measurement.
Experience forecasting commercial or marketing performance.
Experience with DSP or advertising optimisation algorithms.
Experience with modern MLOps tooling and platforms such as Vertex AI.
Experience with applied AI techniques that complement traditional predictive modelling and decision science.
Experience in incrementality or causal inference.

Responsibilities

Partner with the Director of Data Science and business and marketing teams to identify high-value opportunities and translate commercial problems into predictive models and decision-making products.
Prioritise Data Science work around commercial impact, including lifetime value, propensity modelling, customer value, forecasting, and performance marketing optimisation.
Guide the development of reusable models and methodologies that scale across products, verticals, and use cases.
Take modelling work from experimentation through adoption, ensuring outputs are measurable, explainable, and designed for their intended use.
Work with Engineering and ML Ops to deploy successful models as reliable production products.
Ensure models have appropriate experimentation, monitoring, retraining, and feedback loops, and partner with technical teams on infrastructure for operation at scale.
Build trusted relationships across Marketing, Commercial, Analytics, Product, and Engineering.
Translate business needs into Data Science opportunities and complex modelling into clear recommendations, explaining implications and next steps.
Lead, coach, and develop a growing global team of Data Scientists.
Review modelling approaches and technical outputs, establish consistent standards, and contribute hands-on where valuable.
Create an environment that supports technical rigour, curiosity, and commercial thinking.
Measure the real-world impact of the team’s models and products.