Sr. Advanced Data Scientist leveraging advanced analytics and data science at Honeywell. Developing solutions for business growth and operational efficiency in the Atlanta office.
About the role
- Data Scientist developing AI solutions to enhance advertising effectiveness through data-driven insights. Collaborating with teams to innovate and improve products and methodologies for Creative Excellence.
Responsibilities
- contribute to the design and experimentation of the AI models and features that power Creative|Spark AI and related solutions.
- experiment to find alternative measurement and modelling best practices.
- ensure that experimentation and custom analyses remain consistent with Creative|Spark AI global methodology.
- translate research and client briefs into modelling problems and ensure actionable insights and recommendations.
- work autonomously on well-defined problems, collaborate closely with lead data scientists and engineers, and progressively take ownership of more complex modelling workstreams.
- design, engineer, and test new model variants from survey, coded, or digital data sources.
- integrate new features into experimental models and quantify their impact on prediction accuracy, robustness, and interpretability.
- maintain clear experiment logs and documentation so results can be reviewed, reproduced, and reused by CRE and GADS teams.
- act as an advocate for, and owner of, new products and solutions that grow incremental revenue.
Requirements
- Master’s degree (or equivalent) in Data Science, Statistics, Applied Mathematics, Computer Science, Econometrics, or a related quantitative field.
- 7-10 years of professional experience as a Data Scientist in applied machine learning.
- Hands-on experience building and evaluating supervised learning models (regression / classification) in real-world use cases.
- Experience in product management or technical lead roles is a plus
- Experience in marketing, advertising, media, or market research, or predictive modelling on survey, panel, or customer behavior data.
- Prior exposure to production or near-production environments (e.g. working on models that are deployed, monitored, and iterated).
- Strong proficiency in Python and the main data & ML libraries (e.g. pandas, NumPy, scikit-learn, plus optionally TensorFlow / PyTorch / CatBoost / XGBoost).
- Good working knowledge of SQL and experience querying large analytical datasets (e.g. in BigQuery or similar cloud warehouses).
Benefits
- generous PTO
- healthcare plans
- wellness benefits
- flexible workplace policy
- exceptional benefits package
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