AI ML Data Modeling

Responsibilities
Gathers, interprets, and manipulates structured and unstructured data to enable analytical solutions for the business.
Selects the appropriate modeling technique and/or technology with consideration to data limitations, application, and business needs.
Build various ML Models within the Model guidelines and framework.
Consults with peers for guidance, as needed.
Translates business requirements into specific analytical questions, build ML Models and present model outcomes to non-technical business colleagues.
Consults with Data Engineering, IT, the business, and other internal stakeholders to deploy analytical solutions
Stay current with emerging trends and technologies in data quality management, data profiling, data cleansing tools and AI/ML.
Collaborate with data governance teams to ensure compliance with regulatory requirements and industry and legal standards related to data quality and privacy.
Able to identify GenAI use cases given in various business scenarios and come up with possible solutions.
Familiar with various GenAl technologies, Prompt Engineering, RAG etc
Skills & Qualifications
10 to 12 years of relevant experience, and 6+ years of experience in data science, machine learning, quantitative analytics (Mathematics, Statistics or Operational Research etc) roles
Master’s degree in computer science, Statistics, or a related field (Mathematics, Operational Research, Data Science)
Experience in Building and validating statistical, machine learning, and other advanced analytics models.
Experience in Regression (multiple, Logistic etc), Classification (Decision Tree, Random Forest, XGBoost etc) and Time series Forecasting models (ARIMA), Segmentation, NLP, Deep Learning and Graph Analytics.
Experience in Data Mining, Python, R, SQL, and familiarity with ML technologies
Experience in using ML Libraries.
Working Experience in Domino Data Lab, AWS Sagemaker, Snowflake are a plus
Experience using business intelligence tools (e.g. Tableau) and data frameworks (e.g. Hadoop)
Excellent problem-solving, analytical skills and attention to detail, with the ability to identify patterns, trends, and anomalies in data.
Ability to write code that is easy to follow, well documented, and commented where necessary to explain logic (high code transparency).
Experience in querying and preprocessing data from structured and/or unstructured databases using query languages such as SQL, HQL, NoSQL, etc.
Experience in working with structured, semi-structured, and unstructured data files such as delimited numeric data files, JSON/XML files, and/or text documents, images, etc.
Strong communication and collaboration skills, with the ability to effectively interact with technical and non-technical stakeholders.