Forecasting Data Scientist
New
Remote-first working arrangement within IndiaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 10+ years
- Required Skills
- PythonSQLMicrosoft Power BITableauDatabricksPySpark
Requirements
- 10+ years of experience in data science, statistical modeling, or advanced analytics, with strong exposure to demand forecasting and supply chain environments.
- Strong proficiency in Python and PySpark, with hands-on experience building and deploying predictive and statistical models.
- Advanced knowledge of SQL, data manipulation, and working with large-scale datasets in cloud-based environments such as Databricks.
- Solid understanding of forecasting methodologies, time series analysis, segmentation strategies, and error/bias optimization techniques.
- Experience working with BI and visualization tools such as Power BI or Tableau, along with strong data storytelling skills.
- Ability to work with cross-functional teams including demand planners, product, and business stakeholders to translate requirements into models.
- Strong analytical thinking, problem-solving ability, and capability to simplify complex technical concepts for business audiences.
- Bachelor’s or Master’s degree in Statistics, Mathematics, Engineering, Data Science, or a related quantitative field.
Responsibilities
- Develop, maintain, and enhance statistical forecasting models for demand planning, ensuring high accuracy and low bias across SKU-level demand behavior using segmentation approaches.
- Analyze large and complex datasets to identify trends, patterns, and root causes affecting forecast performance and business outcomes.
- Monitor and evaluate model performance regularly (MAPE, bias metrics), performing tuning, post-processing, and refinement to improve forecasting quality.
- Design and implement scalable predictive algorithms using Python and PySpark within Databricks environments to support forecasting and planning processes.
- Collaborate closely with Demand Planning and business stakeholders to gather requirements, explain model outputs, and incorporate feedback into model improvements.
- Contribute to data modeling, KPI development, and visualization efforts using SQL, BI tools, and statistical techniques to support decision-making.
- Drive continuous improvement initiatives, enhancing forecasting frameworks, code efficiency, and overall analytical robustness across the planning ecosystem.
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