The Kraft Group is looking to leverage AI and advanced analytics to solve complex problems, extract insights from large datasets, and build AI-driven models to enhance business operations and drive data-informed decision-making across various industries including manufacturing and sports and entertainment.
Requirements
- 4 to 6 years of experience in data science, machine learning, developing and testing advanced analytical models and ML models
- Experience in data mining with the ability to translate raw data into insights and recommendations
- Strong understanding of statistical analysis, predictive modeling, and data visualization.
- Demonstrable experience with common data science toolkits, such as Python, R, NumPy, TensorFlow, Pandas, scikit-learn, etc.
- Experience working with cloud platforms (AWS, Azure, or GCP) and big data technologies (Hadoop, Spark, Snowflake).
- Knowledge of data engineering principles, including ETL processes and database management.
- Understanding of MLOps practices and model deployment techniques.
Responsibilities
- Design, develop and deploy machine learning models and algorithms for predictive analytics, recommendation systems, and other AI applications.
- Analyze large and complex datasets to generate actionable insights for TKG various business units and functions.
- Build and maintain data pipelines, ensuring data integrity and accessibility for analysis.
- Utilize statistical methods and predictive modeling techniques to forecast trends and improve decision-making.
- Design and implement A/B testing frameworks to evaluate business strategies and product enhancements.
- Work with IT and data engineering teams to optimize data storage, processing, and analytics infrastructure.
- Perform data cleaning, preprocessing, feature engineering and exploratory data analysis to prepare data for modeling.
Other
- Bachelors Degree in Statistics, Economics, Mathematics, Computer Science, or quantitative discipline (Masters Degree is a plus)
- Ability to process, analyze and present complex data sets and analyses in ways that are easy to understand for both technical and non-technical audiences
- Ability to work collaboratively within a diverse team and serve a variety of stakeholders with different priorities
- Must have attention to detail and focused concentration
- Must be able to learn new tasks and complete tasks independently
