Job Description
Job Description
Data Scientist, AI
We are supporting a well-known, consumer-focused brand that is building generative AI directly into its digital products. This role exists because the company wants to move faster, build smarter, and put real AI systems into production that customers actually use.
This is a hands-on builder role for someone who wants ownership. You will not be maintaining legacy models or running disconnected experiments. You will be designing, training, and deploying AI systems from scratch, with a heavy focus on generative AI and the data that makes it work.
Why This Role Exists
Leadership is investing in AI to solve real business problems, not to chase trends. They need someone who can take ambiguous business questions, turn them into AI problems, and deliver working solutions that create measurable impact.
This role bridges theory and execution. You are expected to know the fundamentals, but more importantly, you are expected to ship.
What You Will Work On
- Developing and deploying AI and machine learning models for personalization, recommendations, forecasting, optimization, and intelligent decision-making
- Building generative AI solutions using large language models, including retrieval-augmented generation systems
- Selecting models, architectures, and evaluation approaches based on the business problem, data, and constraints
- Working deeply with structured and unstructured data to enable high-quality AI outputs
- Cleaning, transforming, and engineering features from large, messy, multi-source datasets
- Designing and maintaining data pipelines that support AI training and inference
- Running experiments to validate hypotheses and improve model performance
- Evaluating models for accuracy, bias, and generalization and iterating as needed
- Partnering with MLOps and infrastructure teams to move models into production
- Monitoring models in production and implementing retraining or improvements over time
- Communicating results and tradeoffs clearly to non-technical stakeholders
- Staying current on AI and ML advancements and applying what is useful, not theoretical
- Documenting models, data decisions, and methodologies for reuse and scale
What We Are Looking For
This role is best suited for an experienced AI practitioner who is comfortable owning outcomes.
You should bring:
- 5 or more years of hands-on experience in data science, machine learning, or applied AI
- A proven track record of building and deploying AI models that delivered real business value
- Strong Python skills and experience with modern ML frameworks such as TensorFlow, PyTorch, or scikit-learn
- A solid foundation in statistics, mathematics, and experimental design
- Experience working with supervised and unsupervised learning techniques
- Experience deploying models into production and partnering with MLOps or infrastructure teams
- Comfort working with high-volume, imperfect, multi-format data
- Strong judgment when choosing models, metrics, and architectures
- The ability to translate loosely defined business problems into effective AI solutions
Experience with generative AI, large language models, NLP, cloud ML platforms, big data technologies, model interpretability, or experimentation frameworks is highly valued.
Education
A master’s degree in computer science, data science, statistics, mathematics, physics, or a related quantitative field is expected. A PhD or published research is a plus but not required.
Who Thrives Here
People who succeed in this role are builders. They are comfortable operating in ambiguity, making informed tradeoffs, and taking responsibility for outcomes. They care deeply about data quality, understand the realities of production AI, and want their work to matter.
If you are excited by generative AI, enjoy solving hard data problems, and want to build systems that actually get used, this role is designed for that mindset.