Data Scientist
T
Talon Hiring SolutionsData and Analytics
This role is fully remote with some travel to the home office in Birmingham, ALFull-TimeMiddle
Salary$200,000 – $225,000 / year
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Job Details
- Experience
- 4+ years
- Required Skills
- PythonSQLGitMachine LearningMLFlowSnowflakePandasdbt
Requirements
- 4+ years of hands-on experience in Data Science, Machine Learning, Analytics, or a related field.
- Proven experience building and deploying predictive models in a business environment.
- Strong Python skills, including experience with pandas, scikit-learn, and related data science libraries.
- Strong SQL skills and experience working with large or complex datasets.
- Experience with classification and regression modeling and model evaluation techniques.
- Experience working with Snowflake or a similar cloud data platform.
- Experience with Git, version control, code reviews, and standard software development practices.
- Experience with feature engineering and building datasets for machine learning models.
- Experience in retail, CPG, consumer technology, or consumer hardware.
- Experience with Snowpark, Snowflake ML Functions, dbt, or MLflow.
- Experience working with feature stores.
- Experience delivering machine learning outputs to BI, marketing, customer engagement, or other downstream platforms.
Responsibilities
- Build, deploy, and maintain predictive and prescriptive models that solve real-world business challenges such as customer churn, subscription health, and purchase propensity.
- Transform complex data into actionable insights that support business and customer decisions.
- Develop and maintain features and training datasets within Snowflake and dbt.
- Use Python and SQL to perform feature engineering, statistical analysis, and machine learning.
- Validate and monitor model performance using appropriate metrics such as precision, recall, and AUC.
- Deliver model outputs that can be integrated into BI tools and downstream platforms.
- Partner with Data & Analytics, Engineering, and business stakeholders to define problems, establish success metrics, and deliver impactful solutions.
- Translate ambiguous business questions into well-defined modeling and analytics requirements.
- Document models, features, assumptions, data lineage, experiments, and results to ensure solutions are reliable and reproducible.
- Track and manage experiments using MLflow or similar tools.
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