Data Science Engineer
Job Title: Data Science Engineer
Location: Reading (Hybrid 3 days per week onsite)
Duration: 6 months contract
What You Will Do:
Design and deliver a sophisticated EDU rSAM (remaining sales addressable market) model to quantify untapped revenue opportunities across global education markets.
Partner cross-functionally with key stakeholders to deeply understand the complexities of the education sector, including diverse subscription models, institutional procurement structures, and consortium-based purchasing dynamics.
Collaborate with Data Engineering teams to integrate and operationalise third-party datasets e.g. student population and institutional metrics to create rich, actionable customer intelligence profiles.
Work closely with the Product Marketing and Sales Strategy teams to align analytical outputs with go-to-market strategies, pricing frameworks, and commercial objectives at the product-offering level.
Design, build, and productionise scalable end-to-end data pipelines incorporating normalised customer attributes, behavioural signals, and finalised rSAM outputs.
Combine the EDU rSAM with advanced propensity modelling techniques to optimise education-focused sales motions and accelerate customer growth opportunities.
Deliver high-impact strategic insights on market opportunity sizing and customer propensity trends to senior leadership, enabling data-driven decision-making and long-term commercial planning.
What You Will Need to Succeed:
5+ years of advanced SQL expertise, with a strong track record in querying, cleansing, integrating, and analysing complex datasets at scale, ideally within Databricks environments.
Strong Python capabilities for data manipulation, statistical analysis, and predictive modelling.
Demonstrated success in developing, validating, and continuously optimising data science models that directly contribute to revenue growth and commercial performance.
Experience with propensity modelling and related predictive analytics techniques is highly advantageous.
Exceptional ability to translate complex analytical findings into clear, compelling insights for senior stakeholders and cross-functional audiences.
Strong analytical thinking and problem-solving skills, with proven success operating in fast-paced, high-growth environments with evolving business priorities.
Location: Reading (Hybrid 3 days per week onsite)
Duration: 6 months contract
What You Will Do:
Design and deliver a sophisticated EDU rSAM (remaining sales addressable market) model to quantify untapped revenue opportunities across global education markets.
Partner cross-functionally with key stakeholders to deeply understand the complexities of the education sector, including diverse subscription models, institutional procurement structures, and consortium-based purchasing dynamics.
Collaborate with Data Engineering teams to integrate and operationalise third-party datasets e.g. student population and institutional metrics to create rich, actionable customer intelligence profiles.
Work closely with the Product Marketing and Sales Strategy teams to align analytical outputs with go-to-market strategies, pricing frameworks, and commercial objectives at the product-offering level.
Design, build, and productionise scalable end-to-end data pipelines incorporating normalised customer attributes, behavioural signals, and finalised rSAM outputs.
Combine the EDU rSAM with advanced propensity modelling techniques to optimise education-focused sales motions and accelerate customer growth opportunities.
Deliver high-impact strategic insights on market opportunity sizing and customer propensity trends to senior leadership, enabling data-driven decision-making and long-term commercial planning.
What You Will Need to Succeed:
5+ years of advanced SQL expertise, with a strong track record in querying, cleansing, integrating, and analysing complex datasets at scale, ideally within Databricks environments.
Strong Python capabilities for data manipulation, statistical analysis, and predictive modelling.
Demonstrated success in developing, validating, and continuously optimising data science models that directly contribute to revenue growth and commercial performance.
Experience with propensity modelling and related predictive analytics techniques is highly advantageous.
Exceptional ability to translate complex analytical findings into clear, compelling insights for senior stakeholders and cross-functional audiences.
Strong analytical thinking and problem-solving skills, with proven success operating in fast-paced, high-growth environments with evolving business priorities.
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