Job Description : Data Scientist / AI Specialist
Location: Chennai,
Experience: 5–7 Years
Role Overview
As a Data Scientist/AI Specialist, you will be the primary engine for discovering hidden
insights and building predictive models that drive business value. You are responsible
for the entire analytical lifecycle—from identifying patterns in raw data to implementing
complex models in production. You will bridge the gap between abstract data and
actionable business strategy through advanced statistics, NLP, and Computer Vision.
Key Responsibilities
Advanced Model Development: Design and build high-performance predictive
models and machine learning algorithms to solve complex analytical problems.
Business Intelligence: Translate raw data patterns into business insights using
Power BI to drive executive decision-making.
Validation & Training: Conduct rigorous model training and validation to ensure
accuracy, reliability, and ethical AI standards.
Requirement Analysis: Work closely with stakeholders to understand business
pain points and design technical solutions that capture value.
Documentation: Maintain thorough documentation of data experiments, model
versions, and logic for seamless knowledge transfer
Core Competencies (The Skillet)
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Analytics & Specialization
Algorithms: Mastery of advanced statistics and ML algorithms (Regression,
Clustering, Decision Trees, Neural Networks).
NLP & Vision: Specialized experience in Natural Language Processing (Text
mining, sentiment analysis) and Computer Vision (Object detection, image
classification).
Modeling: Expertise in feature engineering and data modeling to ensure high-
quality "fuel" for AI.
Languages: Expert-level proficiency in Python (Pandas, Scikit-learn) or R.
Environments: Extensive experience using Jupyter Notebooks for
experimentation and Azure ML Studio for production-grade deployments.
Visualization: Ability to build interactive dashboards in Power BI to
communicate insights to non-technical teams.
Insight Discovery: Proven ability to look at unstructured data and find
opportunities for cost savings or revenue growth.
Solution Design: Ability to define the architecture of a data science project
before writing a single line of code.