Responsibilities of the Candidate:

Hands-on experience or strong knowledge in Agentic AI, machine learning, statistical modeling, feature engineering, exploratory data analysis, and supervised/unsupervised/semi-supervised learning.
Strong proficiency in Python and/or R, with solid software development and scripting capabilities.
Experience working with SQL databases and large-scale data platforms, including Snowflake, Teradata, or similar technologies.
Knowledge of the Snowflake ecosystem, including data transformation, analytical workloads, and integration of Snowflake data with Python/ML applications. Experience with Snowpark, Snowflake Cortex AI, or similar AI/ML capabilities is a plus.
Experience with Microsoft Azure technologies, particularly services used for data, analytics, AI, and application deployment. Exposure to Azure Data Factory, Azure Databricks, Azure Machine Learning, Azure OpenAI, ADLS, or related Azure services is desirable.
Ability to extract and integrate data from multiple sources using SQL, APIs, ETL/ELT pipelines, and other query mechanisms.
Strong understanding of data cleansing, feature extraction, outlier detection, missing-data handling, data quality, and data transformation techniques.

Requirements:

Experience working with time-series data, image data, semi-supervised learning, anomaly detection, and datasets with frequently changing distributions is a plus.
Familiarity with SSIS, ETL/ELT processes, data pipelines, and data integration patterns.
Exposure to visualization or application technologies such as Tableau, Power BI, JavaScript, or Angular is beneficial.
Experience working with Manufacturing Execution Systems (MES) or semiconductor/manufacturing datasets is a plus but not required.
Experience with Generative AI, RAG, AI agents, LLM-based applications, prompt engineering, or enterprise AI solutions is highly desirable.
Strong software engineering fundamentals, including writing maintainable, reusable, production-quality code and working with version-control practices.
Strong analytical and problem-solving skills with the ability to translate complex business/manufacturing problems into scalable data and AI solutions.
Strong verbal and written communication skills with the ability to communicate technical findings effectively to both technical and business stakeholders.
Publications at conferences such as CVPR, NeurIPS, ICML, or KDD are a plus; however, this is an applied Data Science position rather than a research role.