Decision Support Data Scientist (Global Manufacturing Analytics)

As a Decision Support Data Scientist within the Manufacturing Analytics and Strategy Execution (MASE) team you will contribute to the development and validation of high-quality insights that support management decision-making and performance management. You will apply decision science statistics forecasting advanced analytics machine learning AI and business understanding to help strengthen manufacturing performance and enable faster more consistent decisions.

Working with experienced team members as part of the Global Manufacturing Control Tower you will translate operational data into clear performance insights driver analyses forecasts scenarios early-warning signals and recommendations. You will help ensure that analytical outputs are reliable explainable and relevant to the decisions leaders need to make.

This role is designed for an early-career professional who is curious analytical and eager to learn. You will build practical experience in manufacturing analytics while gradually taking ownership of defined decision-support use cases and contributing to the question: what is happening why is it happening what may happen next and what actions should be considered

Principal Duties / Responsibilities

• Support the development and validation of Control Tower insights helping ensure that analyses forecasts scenarios and recommendations are accurate explainable and supported by appropriate evidence.

• Work with senior Data Scientists and business stakeholders to translate operational questions into clear analytical tasks assumptions hypotheses data requirements and success criteria.

• Develop decision-support analyses and performance insights across:

  • productivity throughput and capacity

  • quality yield and operational risk

  • delivery shipment and backlog performance

  • cost margin and resource drivers

  • manufacturing network and site performance

• Support the definition and validation of KPI logic baselines targets thresholds and leading indicators and help document how measures should be calculated and interpreted.

• Apply data exploration statistics forecasting diagnostic analytics machine learning and AI methods with guidance selecting approaches that are appropriate for the business question.

• Prepare clear visualisations performance narratives driver analyses early-warning signals and scenario implications that distinguish meaningful signals from noise and highlight areas requiring attention.

• Build and test analytical prototypes using Python SQL R BI tools and cloud technologies; document methods assumptions limitations and validation results to support reproducibility and responsible use.

• Assess data quality and monitor analytical outputs including forecast accuracy bias stability drift and continued business relevance; flag issues and support model improvement when required.

• Collaborate with the Analytics Lead experienced Data Scientists Engineering teams the Manufacturing Strategy & Execution Lead and operational stakeholders to align insights with business priorities and Control Tower deliverables.

• Participate in analytical workstreams from problem framing through validation and value assessment incorporating feedback and gradually taking ownership of defined analyses and decision-support use cases.

• Bachelors or Masters degree in Data Science Statistics Operations Research Computer Science Engineering Business Analytics Economics or a related quantitative field. A Masters degree is preferred.

• One to two years of relevant work internship research or project experience in data science decision science analytics business intelligence or a related field.

• Strong foundation in analytical problem solving with the ability to break down questions test assumptions interpret results and communicate conclusions clearly.

• Practical experience with data cleaning exploratory data analysis data visualisation and preparation of analytical findings through internships academic research capstone projects or work experience.

• Foundational knowledge of statistics forecasting scenario modeling diagnostic or predictive analytics machine learning and AI with interest in applying these methods to business and operational problems.

• Understanding of KPI design baselines targets thresholds leading indicators and basic methods for assessing accuracy bias stability explainability and business relevance.

• Good written and verbal communication skills with the ability to explain analytical methods limitations findings and implications to both technical and non-technical audiences.

• Practical programming experience in Python SQL or R gained through coursework internships research personal projects or employment.

• Experience using data-visualisation tools such as Tableau Power BI Qlik Spotfire or similar to communicate insights and support decision-making.

• Ability to work with imperfect or complex data identify data-quality limitations and maintain attention to detail in analytical work.

• Curiosity willingness to learn openness to feedback and peer review and the ability to collaborate effectively in a global cross-functional environment.

• Ability to manage multiple priorities and deliver well-structured analyses or prototypes in a fast-paced environment.

Preferred Qualifications

• Internship research or project experience related to manufacturing supply chain quality finance operations or enterprise performance management.
• Coursework or project experience in decision science operations research optimisation time-series forecasting or scenario analysis.
• Exposure to cloud platforms such as AWS Azure or Google Cloud or to modern enterprise data platforms.
• Experience analysing data from ERP MES shopfloor systems business systems or other operational datasets.
• Familiarity with Industry 4.0 technologies including IIoT digital twins automation.
• Evidence of applied learning through a portfolio capstone project research publication analytics competition or relevant technical certification.

Additional Details

This job has a full time weekly schedule.

Our pay ranges are determined by role level and location. Within the range individual pay is determined by work location and additional factors including job-related skills experience and relevant education or training. During the hiring process a recruiter can share more about the specific pay range for a preferred location. Pay and benefit information by country are available at: Technologies Inc. is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race color religion sex sexual orientation gender identity national origin protected veteran status disability or any other protected categories under all applicable laws.

25% of the Time

Day

No End Date

Administration