Data Science Specialist

• Define and execute data science strategy for I4.0: Focus on creating value, following a problem solving and delivery approach.
• Develop dashboards and insights in the operations digital portfolio of productivity, quality, competitivity and maintenance applications by executing successful end to end advanced analytics pilots through all phases along stakeholders: Business understanding, data understanding, data preparation (ETLs), modeling, evaluation, and deployment of automated solutions in the company Azure environment.
• Constantly look for improvement opportunities, make hypothesis, test and add value to existing digital products through small but impactful insights, working along the product and experts teams. Utilize your knowledge of statistics and analytics including, supervised, unsupervised and reinforced learning, clustering, propensity modeling, and other explanatory and causal inference techniques to gain insights around equipment and process behaviors.
• Audit and validate development and integration of advanced analytics products in collaboration with the external Metalsa ecosystem (Partners, start-ups, scale-ups).
• Translate business pains into AI use cases for Competitiveness, Adv Manufacturing & I4.0
• Develop and deploy analytics and AI models to support Operations decision-making agility.
• Ensure data quality, integration and visibility for key operations and competitiveness KPIs (e.g. Competitive Edge Files).
• Partner with plants to train and enable local teams in AI & Advanced Analytics adoption.
• Assess potential AI consulting support to accelerate Ops transformation with a focus on learning and future autonomy
• Scout emerging AI tools, libraries, and trends to translate them into innovative, value-driven solutions

• 3+ years of professional experience in data science, ML engineering, applied statistics, or related area of study, preferably (but not limited to) in the manufacturing industry.
• Advanced English level

• Data mining: Expert use of data mining methodologies such as regression, decision trees, time series analysis, machine learning, and text mining.
• Data Analysis tools: Python (pandas, numpy) and modeling (sckitlearn, statsmodels, etc).
• Databases: relational databases including SQL and non-relational databases and data schemas like time series, data lakes, etc.
• Microsoft Azure architectures and tools like Azure ML, Azure databases, Azure cognitive services, Azure Databricks, Azure Datafactory, Batch, Jupyter notebooks (or other cloud service)
• Data Engineering: Maintaining data pipelines, evolving data ETLs, getting insights from messy (unclean, disorganized) data and having a good degree of autonomy as you work.
• Data visualization tools like PowerBI, Seaborn, Matplotlib, Shiny R, etc.
• Data architecture.

• Ability to manage multiple projects efficiently and able to meet deadlines.
• Demonstrates ability to improve processes and develop solutions that can be adapted to additional customers.
• Recognized as an advanced subject matter expert of analytic products, applied technologies and processes.
• Results/delivery oriented.
• Strong business focus. Must excel at connecting business requirements to data mining objectives and to measurable business benefit.
• Self-learning
• Research & Innovation

Metalsa