You will develop and deploy machine learning models to solve complex business problems using structured and unstructured data.
Responsibilities
Build and validate machine learning models, including regression, classification, time series forecasting, segmentation, NLP, deep learning, and graph analytics.
Translate business requirements into specific analytical questions and present model outcomes to non-technical stakeholders.
Identify GenAI use cases and design solutions using prompt engineering and RAG.
Collaborate with Data Engineering, IT, and data governance teams to deploy solutions and ensure regulatory compliance.
Manipulate and preprocess data from structured, semi-structured, and unstructured sources including SQL, NoSQL, JSON, and XML.
Required Skills
10 to 12 years of relevant experience, with 6+ years specifically in data science, machine learning, or quantitative analytics.
Master’s degree in Computer Science, Statistics, Mathematics, Operational Research, or Data Science.
Proficiency in Python, R, and SQL.
Extensive experience with data mining and machine learning libraries.
Expertise in statistical modeling, including Logistic Regression, Decision Trees, Random Forest, XGBoost, and ARIMA.
Ability to write transparent, well-documented, and commented code.
Experience querying and preprocessing data from various database types and file formats.
Strong analytical skills to identify patterns, trends, and anomalies in complex datasets.
Preferred Skills
Working experience with Domino Data Lab, AWS SageMaker, or Snowflake.
Experience with Tableau, Hadoop, or other business intelligence and data frameworks.