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Job Description & How to Apply Below
A fast-scaling analytics and AI solutions provider operating across India, we empower enterprises to transform raw data into predictive insights, automated decision engines, and customer-centric AI products. Our Data Scientists work at the intersection of business strategy and machine learning—shipping models that drive revenue, reduce churn, and optimize operations for clients in fintech, retail, and healthcare. We value technical depth, production mindset, and the ability to translate ambiguity into deployable solutions.
Role & Responsibilities
Design, train, and deploy ML models (supervised/unsupervised) that solve real business problems—customer segmentation, fraud detection, demand forecasting.
Translate business KPIs into measurable ML objectives and define success metrics aligned with stakeholder outcomes.
Build end-to-end pipelines: data cleaning ➜ feature engineering ➜ model training ➜ A/B testing ➜ MLOps deployment.
Collaborate with engineers to product ionize models using cloud platforms (AWS/GCP) and containerization tools (Docker, Kubernetes).
Conduct statistical analysis to validate model performance, detect bias, and recommend retraining triggers.
Present insights and model logic to non-technical stakeholders using dashboards (Tableau, Power BI) and clear visual storytelling.
Skills & Qualifications
Must-Have
Python
Scikit-learn
Pandas
Num Py
SQL
Machine Learning
Model Evaluation Metrics
Feature Engineering
Preferred
Tensor Flow or Py Torch
Spark MLlib
Cloud Platforms (AWS Sage Maker / GCP Vertex AI)
Benefits & Culture Highlights
Work onsite with cross-functional teams in a collaborative, innovation-driven environment.
Access to upskilling programs, conference stipends, and internal AI hackathons.
Impact-driven work—your models directly influence client ROI and operational efficiency.
Skills:
learning,data,machine learning,ml
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