Applied & Data Scientist

As a Senior Data Scientist, your role involves designing, developing, and deploying advanced AI/Data Science solutions with a focus on scalability and ethical AI. You will apply your expertise in machine learning frameworks and advanced techniques such as LLM fine-tuning, RAG implementations, and evaluation methods. Conducting applied research in areas like fraud detection, risk modeling, and automation will be part of your responsibilities. Additionally, you will perform data analysis and profiling on large datasets using SQL/KQL, collaborate with data engineers, and create Power BI dashboards aligned with business use cases. Your role will also require translating business needs into technical requirements, supporting data governance initiatives, and partnering with various teams in an Agile environment. Key Responsibilities: - Design, build, and deploy advanced AI/Data Science solutions with a focus on scalability and ethical AI. - Apply expertise in machine learning frameworks and advanced techniques (LLM fine-tuning, RAG implementations, evaluation methods). - Conduct applied research in areas such as fraud detection, risk modeling, and automation. - Perform data analysis and profiling on large datasets (SQL/KQL), ensuring high-quality insights. - Collaborate with data engineers to validate pipelines, transformations, and ensure data accuracy. - Create and maintain Power BI dashboards aligned with business use cases. - Translate business needs into technical requirements and document business rules, logic, and lineage. - Support data governance initiatives, metadata management, and gap analysis of current vs. target data flows. - Partner with engineering, product, and program management teams in an Agile environment. Qualifications Required: - 8+ years with a Bachelors degree in Statistics, Mathematics, Physics, Econometrics, Computer Science, Engineering, or related field; or - 6+ years with a Masters degree in the same fields; or - 2+ years with a Ph.D. in the same fields. - Proven expertise in statistical modeling, feature engineering, model evaluation, and data pipeline design. - Strong command of SQL/KQL for querying and profiling large datasets. - Experience with data mapping, source-to-target documentation, and profiling. - Familiarity with modern data platforms (Microsoft Azure, Data Factory, Data Lake, Microsoft Fabric preferred). - Strong communication skills with the ability to engage both technical and non-technical stakeholders. Please note that no additional details about the company were provided in the job description.,