Collaborate with business stakeholders to identify opportunities and translate business challenges into actionable Data Science and AI use cases.
Design, develop and evaluate machine learning models using statistical methods, supervised and unsupervised learning techniques.
Develop predictive analytics solutions for business use cases such as customer behavior analysis, risk assessment, forecasting and process optimization.
Explore and implement Generative AI and Large Language Model (LLM) solutions, including Retrieval-Augmented Generation (RAG) and intelligent automation.
Perform exploratory data analysis, feature engineering and model experimentation on large and complex datasets.
Develop scalable and reusable Data Science components using Python, SQL and Azure Databricks.
Collaborate with Data Engineers and Cloud Platform Engineers to deploy, monitor and continuously improve machine learning models in production.
Ensure model quality, explainability and reliability through appropriate evaluation, validation and monitoring techniques.
Communicate analytical findings, model performance and business impact to technical and non-technical stakeholders.
Contribute to Data Science best practices, reusable methodologies and continuous improvement of the enterprise AI & Data Platform.

5+ years of experience in Data Science, Applied Machine Learning, Advanced Analytics or a related field.
Strong hands-on experience with Python and SQL, including commonly used Data Science libraries such as pandas, NumPy and scikit-learn.
Solid understanding of statistics, probability, machine learning algorithms and predictive modeling techniques.
Proven experience developing and evaluating supervised and unsupervised machine learning models, including classification, regression and clustering.
Experience with feature engineering, data pre-processing, model validation and performance evaluation.
Practical experience working with large, complex datasets and combining data from multiple sources.
Experience with Generative AI, Large Language Models (LLMs) or Natural Language Processing (NLP) solutions.
Familiarity with cloud-based data platforms and distributed data processing, preferably Microsoft Azure and Databricks.
Experience with version control systems such as Git and collaborative software development practices.
Ability to translate business requirements into analytical approaches and communicate complex technical concepts to business stakeholders.
Strong analytical and problem-solving skills, with the ability to work independently and take end-to-end ownership of Data Science initiatives.
Excellent professional communication skills in English.

Collaborate with business stakeholders to identify opportunities and translate business challenges into actionable Data Science and AI use cases.
Design, develop and evaluate machine learning models using statistical methods, supervised and unsupervised learning techniques.
Develop predictive analytics solutions for business use cases such as customer behavior analysis, risk assessment, forecasting and process optimization.
Explore and implement Generative AI and Large Language Model (LLM) solutions, including Retrieval-Augmented Generation (RAG) and intelligent automation.
Perform exploratory data analysis, feature engineering and model experimentation on large and complex datasets.
Develop scalable and reusable Data Science components using Python, SQL and Azure Databricks.
Collaborate with Data Engineers and Cloud Platform Engineers to deploy, monitor and continuously improve machine learning models in production.
Ensure model quality, explainability and reliability through appropriate evaluation, validation and monitoring techniques.
Communicate analytical findings, model performance and business impact to technical and non-technical stakeholders.
Contribute to Data Science best practices, reusable methodologies and continuous improvement of the enterprise AI & Data Platform.

5+ years of experience in Data Science, Applied Machine Learning, Advanced Analytics or a related field.
Strong hands-on experience with Python and SQL, including commonly used Data Science libraries such as pandas, NumPy and scikit-learn.
Solid understanding of statistics, probability, machine learning algorithms and predictive modeling techniques.
Proven experience developing and evaluating supervised and unsupervised machine learning models, including classification, regression and clustering.
Experience with feature engineering, data pre-processing, model validation and performance evaluation.
Practical experience working with large, complex datasets and combining data from multiple sources.
Experience with Generative AI, Large Language Models (LLMs) or Natural Language Processing (NLP) solutions.
Familiarity with cloud-based data platforms and distributed data processing, preferably Microsoft Azure and Databricks.
Experience with version control systems such as Git and collaborative software development practices.
Ability to translate business requirements into analytical approaches and communicate complex technical concepts to business stakeholders.
Strong analytical and problem-solving skills, with the ability to work independently and take end-to-end ownership of Data Science initiatives.
Excellent professional communication skills in English.

Senior Data Scientist (B-1227)

Skills