Machine Learning Engineer

Hiring : Classical ML Engineer

Experience : 5-8 Years

Location : Gurugram / Chennai / Bengaluru (Hybrid)

Employment Type : Full-Time

About the Role :

We are looking for a highly skilled Classical ML Engineer to design, develop, and deploy machine learning solutions that solve complex business problems and drive data-driven decision-making. The ideal candidate will possess strong expertise in machine learning algorithms, predictive analytics, statistical modeling, and large-scale data processing. This role will focus on developing and implementing machine learning models for forecasting, customer analytics, optimization, commercial analytics, and business intelligence use cases. The candidate will collaborate closely with Data Scientists, Business Stakeholders, Product Teams, and Engineering Teams to build scalable and production-ready ML solutions.

Key Responsibilities :

1. Machine Learning Development :

- Design, develop, and deploy machine learning models using classical machine learning techniques.

- Implement supervised and unsupervised learning algorithms to solve business challenges.

- Build predictive models for customer behavior, demand forecasting, sales optimization, and business performance.

- Perform feature engineering, model training, validation, tuning, and performance optimization.

- Develop scalable ML pipelines and model deployment frameworks.

2. Forecasting & Predictive Analytics :

- Build and maintain forecasting models to support business planning and decision-making.

- Develop time-series forecasting solutions for demand planning, revenue forecasting, and operational optimization.

- Analyze historical data, trends, seasonality, and external factors impacting business performance.

- Continuously improve forecasting accuracy through advanced modeling techniques.

3. Data Analysis & Statistical Modeling :

- Conduct exploratory data analysis and identify meaningful business insights.

- Apply statistical techniques to uncover trends, correlations, and predictive signals.

- Perform hypothesis testing, regression analysis, clustering, and classification modeling.

- Translate analytical findings into actionable business recommendations.

4. Model Deployment & Engineering :

- Collaborate with engineering teams to deploy machine learning models into production environments.

- Develop automated workflows for model monitoring, retraining, and performance tracking.

- Ensure model scalability, reliability, and maintainability.

- Support model governance and lifecycle management processes.

5. Stakeholder Collaboration :

- Work closely with business stakeholders to understand analytical requirements.

- Translate business challenges into machine learning and analytics solutions.

- Present findings, model outcomes, and recommendations to cross-functional teams.

- Partner with product and technology teams to deliver business impact through AI and analytics.

Required Skills & Experience :

- 5-8 years of experience in Machine Learning, Data Science, Predictive Analytics, or Advanced Analytics.

- Strong expertise in Classical Machine Learning algorithms.

- Hands-on experience in model development, deployment, and optimization.

- Advanced proficiency in Python and SQL.

- Strong understanding of statistics, probability, and predictive modeling techniques.

- Experience working with large structured datasets and business analytics problems.

Mandatory Skills :

- Machine Learning, Classical ML Algorithms, Python, SQL, Predictive Analytics, Statistical Modeling, Data Science, Regression Models, Classification Models, Clustering Techniques, Feature Engineering, Model Validation.

Preferred Skills :

- Time Series Forecasting, XGBoost, LightGBM, CatBoost, Scikit-Learn, Pandas, NumPy, Forecasting Models, Business Analytics, MLOps.

Key Competencies :

- Analytical Thinking, Problem Solving, Statistical Analysis, Business Acumen, Data Interpretation, Stakeholder Management, Communication Skills, Decision-Making Support.

Why Join Us :

- Opportunity to work on high-impact machine learning and analytics initiatives.

- Exposure to large-scale business problems across multiple domains.

- Collaborative environment with strong focus on innovation and data-driven decision-making.

- Opportunity to build scalable ML solutions that directly impact business outcomes.

(ref:hirist.tech)