How you will contribute

Analyze and derive value from data through the application methods such as mathematics, statistics, computer science, machine learning and data visualization. In this role you will also formulate hypotheses and test them using math, statistics, visualization and predictive modeling
Understand business challenges, create valuable actionable insights about the data, and communicate your findings to the business. After that you will work with stakeholders to determine how to use business data for business solutions/insights
Enable data-driven decision making by creating custom models or prototypes from trends or patterns discerned and by underscoring implications. Coordinate with other technical/functional teams to implement models and monitor results
Apply mathematical, statistical, predictive modelling or machine-learning techniques and with sensitivity to the limitations of the techniques. Select, acquire and integrate data for analysis. Develop data hypotheses and methods, train and evaluate analytics models, share insights and findings and continues to iterate with additional data
Develop processes, techniques, and tools to analyze and monitor model performance while ensuring data accuracy
Evaluate the need for analytics, assess the problems to be solved and what internal or external data sources to use or acquire. Specify and apply appropriate mathematical, statistical, predictive modelling or machine-learning techniques to analyze data, generate insights, create value and support decision making
Contribute to exploration and experimentation in data visualization and you will manage reviews of the benefits and value of analytics techniques and tools and recommend improvements

What you will bring

Strong quantitative skillset with experience in statistics and linear algebra.
A natural inclination toward solving complex problems
Knowledge/experience with statistical programming languages including R, Python, SQL, etc, to process data and gain insights from it
Knowledge of machine learning techniques including decision-tree learning, clustering, artificial neural networks, etc, and their pros and cons
Knowledge and experience in advanced statistical techniques and concepts including, regression, distribution properties, statistical testing, etc
Good communication skills to promote cross-team collaboration
Multilingual coding knowledge/experience: Java, JavaScript, C, C++, etc
Experience/knowledge in statistics and data mining techniques including random forest, GLM/regression, social network analysis, text mining, etc Ability to use data visualization tools to showcase data for stakeholders

More about this role

You will represent and communicate data requirements to help us understand our data assets and the relationships among them. You will investigate, analyze and scope data requirements to support the development of data integration, data retrieval and reusable data sets.

What you need to know about this position:

The Data Scientist will be responsible for advanced forecasting methodologies for demand forecasting to generate better forecasting results in terms of accuracy and bias

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 make the predictive process more efficient and accurate .

What extra ingredients you will bring:

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

Education / Certifications:

Bachelors Degree required . Masters in quantitative field of Statistics, Applied Mathematics or Engineering, with specific full-time courses in Analytics preferred
Certifications Google Cloud Platform as Data Scientist, or Data Analyst , or in Databricks (nice to have)
Experience in any Data Science language like Python (preferred), PySpark (preferred), R, Julia .
Experience developing and deploying Statistical and/ or machine learning solutions.
Knowledge and experience in data science cloud platforms like Databricks (preferred) or similar .
+5 years of a pplied knowledge of analytical techniques in statistical modelling, machine learning with exposure to forecasting domain especially driver-based forecasting
Experience on working with FMCG, Food & Beverages, Retail or similar industry data with understanding the business process will be an advantage (nice to have)
Should be able to articulate data science outcome into business understandable language
Experience leading projects and/or processes
Fluent English is a must

Job specific requirements:

A desire to drive your future and accelerate your career and the following experience and knowledge:
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.

Data Scientist

Forecasting Data Scientist