Senior Demand Data Scientist

3 semanas atrás

São Paulo, Brasil AkzoNobel Tempo inteiro

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Senior Demand Data Scientist

We are AkzoNobel. You probably know us.

We are present in homes, buildings, boats, cars—basically, on every surface where there is an opportunity to bring more color, life, and protection, in over 150 countries that use our paints and coatings. To continue painting a better future, we need you

We are looking for a Senior Demand Data Scientist to work at our Morumbi office.

Job Purpose:

The main objective is to monthly generate the best fit statistical forecast model possible for the company's main business units globally, providing this Baseline to be data-driven for building the Demand Plan (DMR) within the IBP cycle.

Statistical Forecast is the base across Akzo’s Nobel business units for the development of the IBP’s Demand Plan.

Demand Hub must keep generating the Statistical Forecast for 58 Commercial Units within 8 Business Units from Decorative Paints and Coatings.

Focusing on Automation and improvements in KPIs Results: Forecast Accuracy, Forecast BIAS, Forecast Value Added.

Responsibilities:

  • 75% Specialist Hands-On: Managing 8 Commercial Units.
  • 25% Focus on CI: Automation and Results. Managing the projects, getting support from the team, delegating the tasks, and defining the priorities.
  • Generate statistical forecast:
    • Ensure the data quality that will be used as a Statistical Base Forecast, identifying, and flagging data anomalies. Reviewing and applying guided corrections whenever necessary.
    • Ensure the flow of activities and execution of established procedures for generating the monthly statistical forecast.
    • Execute some operational procedures based on guided interactions.
  • Connecting and Interacting with Demand Planners:
    • Identifying and removing Outliers together with the demand planning areas, ensuring it will be uploaded in our main tool and the historical base will be cleaned up.
    • Analysis of Outliers automatically identified by the statistical engine system.
    • Mapping of Phase In/Out items, aligning with the demand areas to upload in the system.
  • Forecast performance analysis:
    • Focus on statistical forecasts of the most representative and most critical items based on the ABC/XYZ curve.
    • Focus on the long term, m3 onwards.
    • Evaluate whether the results of the main KPIs meet expectations, Accuracy, BIAS, and FVA (Forecast Value Added), in Lag-1, Lag-3, and Lag-6. If they are below the expected results, understand the reasons and propose and implement solutions to achieve the goals.
    • Conduct monthly meetings to align statistical forecasts with business units, results and outlook, seasonality, trends, and levels. Explaining in detail the reasons for what is changing between cycles, updates considering the most recent results, or regarding improvements.
  • Evaluate and test different methods and statistical models: at different calculation levels to obtain the best results, aligning with the demand team of each business unit. The statistical tool currently used is OMP.
  • Explore alternatives: on how we can achieve better results in terms of Accuracy and BIAS (reducing forecast error). Proactively seek means and methods or auxiliary tools to enhance the forecast performance and overwrite the statistical forecast in OMP whenever necessary.
  • Follow up on improvements: already mapped to ensure the effectiveness of actions and conclusion of developments in Quality and Production environments.
  • Consistent communication: with internal customers (demand planners, demand managers), to understand their needs, identify opportunities for improvement, propose solutions, monitor their effectiveness, and align expectations considering our restrictions, which is making them aware of what is viable, always with transparency.
  • Achieve credibility: of our internal customers, demonstrating the value our area adds to the demand plan consensus.
  • Align with Demand Planners and Managers: on potential leading indicators (exogenous variables) containing high correlation with sales history and how could influence the forecast.

Key Performance Indicators:

  • Timely availability of the Statistical Forecast
  • Forecast Accuracy and Bias
  • Forecast Value Added (Statistical)

Education:

  • Bachelor’s degrees in Math, Statistics, Engineering, or Data Science.

Experience:

  • Solid experience as a Statistician and Data Scientist with a focus on demand forecasting (sales/consumption).
  • Experience with Demand Planning and Supply Chain (competitive advantage).
  • Significant level of experience in Data Analysis tools and languages.

Specific knowledge:

  • Experience with the tools and technology below:
    • Advanced Excel: ability to handle data, knowledge of statistical resources within Data Analysis, Solver, Macros, VBA (Desired)
    • Python or R
    • Azure Data Platform: Databricks (Desired)
    • Power BI
    • OMP forecasting tool (Desired)
    • Experience and understanding of statistical modeling.
    • Fluency in English: for BU support on a global level, constant communication with several countries.

Location:

  • Morumbi

We want to get to know you, and we invite you to apply.
AkzoNobel, together we paint a better future.

All qualified candidates will be considered for the selection process, regardless of race, color, religion, gender, sexual orientation, gender identity, nationality, age, or disability.

At AkzoNobel we are highly committed to ensuring an inclusive and respectful workplace where all employees can be their best self. We strive to embrace diversity in a context of tolerance. Our talent acquisition process plays an integral part in this journey, as setting the foundations for a diverse environment. For this reason we train and educate on the implications of our Unconscious Bias in order for our TA and hiring managers to be mindful of them and take corrective actions when applicable. In our organization, all qualified applicants receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age or disability.

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