Machine Learning Engineer

Employees in this role are responsible for extracting insights and predicting trends from complex datasets by applying advanced data science and machine learning techniques
. The position supports data‑driven decision‑making across the organization by designing, developing, and deploying scalable analytics and ML solutions.

This role requires strong analytical thinking, deep technical expertise, and the ability to translate complex model outputs into clear, actionable business insights
.

• Partner with business stakeholders to understand requirements
  , assess data availability, and determine the most effective analytical approaches.
• Analyze large, complex datasets to uncover meaningful patterns, relationships, and trends that inform strategic decisions.
• Translate business questions into testable hypotheses and measurable analytical outcomes.

• Design, develop, and implement machine learning and statistical models (supervised, unsupervised, and predictive) to solve business problems.
• Apply advanced analytics techniques including data mining, predictive modeling, prescriptive analytics, statistical inference, and machine learning algorithms
  .
• Optimize existing models to improve accuracy, robustness, and computational efficiency
  .

• Design efficient data ingestion, data augmentation, and feature engineering pipelines to support high‑quality model development.
• Build scalable and automated analytics solutions suitable for production deployment
  .
• Ensure models and analytics workflows are maintainable, reproducible, and aligned with software engineering best practices.
• Lead the design and analysis of experiments (e.g., A/B testing, multivariate testing) to validate hypotheses and measure business impact.
• Evaluate model performance using appropriate metrics and ensure results are statistically sound.

• Algorithms
• Python

• Professional
  
  Experience:
  
  5+ years of experience in a Data Science or Machine Learning role with demonstrated business impact.
• Programming: Expert proficiency in Python (Pandas, Num Py, Sci Py, Scikit‑learn) or R.
• Machine Learning: Strong foundation in ML techniques including Gradient Boosting (XGBoost/Light
  
  GBM), Random Forests, GLMs, and Clustering
  .
• SQL: Advanced experience querying and manipulating data from large, distributed databases.
• Mathematics & Statistics: Solid understanding of linear algebra, calculus, probability, and statistical inference
  .
• Software Practices: Experience using Git
  , writing clean, modular, and maintainable code.

• Experience Level: Senior Associate (3–5 years in a relevant field)
• Education Required: Master’s Degree
• Work Arrangement: Hybrid – 4 days onsite per week

• Develop and deploy ML solutions for use cases such as churn prediction, recommendation systems, forecasting, and optimization
  .
• Support model monitoring, performance evaluation, and model drift detection in partnership with engineering teams.
• Ensure analytics solutions are aligned with automation, scalability, and production reliability standards.
• Contribute to a culture of technical excellence, continuous improvement, and data‑driven decision‑making
  .