Job Title: Data Scientist - Platform Modernization (Ref: )
Location: Atlanta, Georgia (Hybrid - 3 days onsite)
Forsyth Barnes is partnering with a leading global consumer organisation to appoint a Data Scientist - Platform Modernization within its growing AI & Data Science function.
This is an exciting opportunity to join a team focused on modernising enterprise Machine Learning capabilities and building scalable production AI solutions. Working closely with Product, Engineering and business stakeholders, you'll build intelligent data products that solve complex commercial challenges whilst helping shape the organisation's next generation AI platform.
This opportunity is ideal for a hands‑on Data Scientist who enjoys taking Machine Learning models from concept through production, working across the full model lifecycle and influencing business decisions through advanced analytics.
Key Responsibilities
- Design, build and deploy production Machine Learning models to solve complex business problems.
- Partner with Product, Engineering and business stakeholders to identify opportunities for AI and advanced analytics.
- Develop predictive models using structured and unstructured data to support commercial decision-making.
- Build scalable feature engineering, model training and deployment pipelines.
- Support the modernisation of enterprise Machine Learning platforms and production workflows.
- Monitor, evaluate and improve model performance within production environments.
- Perform exploratory data analysis to uncover trends, patterns and new opportunities.
- Contribute to AI initiatives, including Generative AI and Large Language Models where appropriate.
- Present technical findings and recommendations to both technical and non-technical stakeholders.
- Champion best practices across Data Science, Machine Learning and model deployment.
Required Experience
- 3-7+ years' commercial experience within Data Science or Machine Learning.
- Strong hands‑on experience building and deploying production Machine Learning models.
- Experience across feature engineering, exploratory data analysis and predictive modelling.
- Strong understanding of the end-to-end Machine Learning lifecycle.
- Experience partnering with Product, Engineering and business stakeholders.