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Mondelēz International · 6 hours ago

Forecasting Data Scientist

East Hanover, NJ

Full-time

Hybrid

Mid, Senior Level

$84K/yr - $115K/yr

5+ years exp

Mondelēz International is a leader in the future of snacking, and they are seeking a Forecasting Data Scientist to enhance their demand forecasting methodologies. The role involves leading statistical forecasting processes, managing data modeling, and collaborating with demand planners to improve forecasting accuracy and efficiency.

Consumer GoodsFood and BeverageFood ProcessingManufacturing

H1B Sponsor Likely

Responsibilities

Lead statistical forecasting process to provide business a statistical base forecast for its demand planning process

Lead some continuous improvement projects to get a more efficient, robust, and accurate models to predict

Analyze business requirements as a guide for data modeling and apply data analysis, design, modeling, and quality assurance techniques, based on a detailed understanding of business processes, to establish, modify or maintain data structures and associated components (entity descriptions, relationship descriptions, attribute definitions

Manage the iteration, review and maintenance of data requirements and data models and you will assist in creating the sematic layer of data

Choose a suitable data modeling approach for each project, such as by assessing the suitability of existing data models and build data models with the flexibility to change when business requirements change

Reconcile multiple logical source models into a single, logically consistent model and you will ensure that the proposed model follows data architecture guidelines and best practices

Design, build, implement and/or maintain Python/PySpark algorithms to execute predictive analysis which help to improve planning processes

Analyse code, define and implement strategies to have more accurate and efficient results

Determine, create and maintain the best Statistical models be to be used, by considering SKU demand behavior using segmentation strategy, to generate high quality demand statistical forecast with low forecast error and bias

Refine forecasting models, by reviewing forecast performance and incorporating feedback from the Demand Planner, to improve forecast error and bias metrics

Analyze the model performance every month / week where MAPE (Main Absolute Percentage Error) is deteriorating and post process the output and if required finetune the output

Propose additional strategies to have a better code (efficient or more accurate) to execute in Databricks

Collaborate with Demand Planners to provide explainability of models and gather feedback to improve the models

Lead, develop, and deploy some algorithms built on databricks that help to the predictive process to be more efficient and accurate

Qualification

Data modelingStatistical forecastingPythonDatabricksMachine learningCloud computingFMCG experienceEnglish fluencyCommunicationProject managementTeam collaboration

Required

Bachelor's Degree required

5+ years of experience in handling multiple projects leading in the domain of supply chain and forecasting with multiple team members as part of org structure

Extensive experience in data modeling in a large complex business with multiple systems

Ability to simplify complex problems and communicate them to a broad audience

Design, build, implement and/or maintain Python/PySpark algorithms to execute predictive analysis which help to improve planning processes

Analyze code, define, and implement strategies to have more accurate and efficient results

Working in cloud computing environments with tools like databricks and help MDLZ to have better practices on it

Aligning and coordinating activities with different areas and stakeholder to guarantee a good execution of projects and processes

Determine, create and maintain the best Statistical models to be used, by considering SKU demand behavior using segmentation strategy, to generate high quality demand statistical forecast with low forecast error and bias

Refine forecasting models, by reviewing forecast performance and incorporating feedback from the Demand Planner, to improve forecast error and bias metrics

Analyze the model performance every month/week where MAPE (Main Absolute Percentage Error) is deteriorating and post process the output and if required finetune the output

Propose additional strategies to have a better code (efficient or more accurate) to execute in Databricks

Collaborate with Demand Planners to provide explainability of models and gather feedback to improve the models

Lead, develop, and deploy some algorithms built on databricks that help to the predictive process to be more efficient and accurate

Experience leading projects and/or processes

Fluent English is a must

Experience developing and deploying Statistical and/or machine learning solutions

Preferred

Masters in quantitative field of Statistics, Applied Mathematics or Engineering, with specific full-time courses in Analytics

Certifications Google Cloud Platform as Data Scientist, or Data Analyst, or in Databricks

Experience on working with FMCG, Food & Beverages, Retail or similar industry data with understanding the business process

Experience in any Data Science language like Python, PySpark, R, Julia

Knowledge and experience in data science cloud platforms like Databricks or similar

Benefits

Health insurance

Wellness and family support programs

Life and disability insurance

Retirement savings plans

Paid leave programs

Education related programs

Paid holidays and vacation time

Company

Mondelēz International

Glassdoor4.0

Mondelēz International, Inc.

Founded in 1903

Chicago, Illinois, USA

10001+ employees

https://www.mondelezinternational.com/

H1B Sponsorship

Mondelēz International has a track record of offering H1B sponsorships. Please note that this does not guarantee sponsorship for this specific role. Below presents additional info for your reference. (Data Powered by US Department of Labor)

Distribution of Different Job Fields Receiving Sponsorship

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Trends of Total Sponsorships

2025 (3)

2024 (1)

2022 (1)