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Quantiphi

Senior Machine Learning Engineer - Traditional

Posted 5 Days Ago
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In-Office
Mumbai, Maharashtra
Senior level
In-Office
Mumbai, Maharashtra
Senior level
Develop and fine-tune traditional machine learning and predictive models for regression, classification, sales forecasting, and growth prediction. Prepare and unify data from multiple sources, perform exploratory data analysis and feature engineering, evaluate model performance, and use Python, Jupyter, and AWS services including S3 and Redshift. Present model outputs and insights to business stakeholders while working independently with client teams.
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While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.
If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!

JOB ROLE - ML Engineer

As an  ML Engineer This role focuses on predictive modeling development utilizing Jupyter Notebook environments alongside AWS cloud infrastructure services.

Must have Skills:

  • Must be capable of working independently with minimal supervision alongside the client's business/technical team

  • 4+  years experience in Traditional Machine Learning & Predictive Modeling: Hands-on experience building and fine-tuning ML models for regression/classification tasks, specifically sales forecasting or growth prediction using time-series and tabular data

  • Python & ML Libraries: Strong command of Python with libraries such as Scikit-learn, Pandas, NumPy, and Jupyter Notebooks for model development and output presentation.

  • Model Testing and evaluation

  • Feature Engineering & EDA

  • AWS Data Ecosystem: Working knowledge of AWS S3 and Amazon Redshift for data ingestion, storage, and retrieval in a cloud development environment

  • Data Preparation & Quality: Experience in data handling, missing values, duplicates, inconsistencies, and building unified analytical datasets from multiple sources

Good to have skills:

  • Store/Retail Domain Knowledge: Understanding of retail KPIs, store segmentation frameworks, and business cockpit/reporting concepts

  • Stakeholder Communication: Ability to present model outputs, performance metrics, and insights to non-technical business stakeholders during weekly review cadences

If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!

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