Machine Learning Engineer

Machine Learning Engineer

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!

Role: Machine Learning Engineer

Experience Level: 1 to 4 Years

Work location: Mumbai

Role Overview:

We are looking for a Machine Learning Engineer to join our growing Data & Digital team. The role involves contributing to Advanced Analytics (AA), Machine Learning (ML), and data-driven projects aimed at improving business processes across various verticals. The ideal candidate should possess strong technical skills in Python, ML model development, and problem-solving, along with the ability to interact with business stakeholders.

Key Responsibilities:

Develop and deploy machine learning models (classification & regression) to solve real-world business problems.

Work across the full analytics lifecycle: from gathering requirements to data preprocessing, modeling, validation, and implementation.

Break down complex business problems into smaller, manageable components and solve them using ML/AA techniques.

Collaborate with cross-functional teams to understand business requirements and translate them into analytical solutions.

Deliver descriptive, predictive, and prescriptive models tailored to business objectives.

Work on digital transformation initiatives to drive efficiency and innovation through advanced data-driven solutions.

Prepare and present data insights and analytical outcomes to stakeholders using visualization tools.

Required Skillset:

Strong proficiency in Python (or R) for data manipulation and model development.

Hands-on experience with key Python libraries: numpy, pandas, seaborn, matplotlib, sklearn.

Solid understanding of machine learning models and algorithms, especially in classification and regression.

In-depth knowledge of algorithms such as Decision Trees, Random Forests, Bagging, Boosting (e.g., XGBoost, LightGBM).

Understanding of fundamentals of statistics and how they apply to data modeling.

Experience with Jupiter Notebook and Databricks for model development and experimentation.

Practical experience in implementing ML models in real business scenarios.

Ability to align ML/AA solutions with business goals and KPIs.

Strong problem-solving and critical thinking abilities.

Experience in stakeholder management.

Proficiency in presentation and visualization tools: MS Power BI, MS Excel.