The ideal candidate is passionate about data, machine learning, scale, and delivering production-ready AI solutions. You will use your strong analytical mindset and collaboration skills to solve complex business problems, build scalable ML systems, and drive data-driven decision making.
Responsibilities
- Analyze and preprocess raw data by assessing quality, cleaning, and structuring it for modeling
- Develop and deploy machine learning models for regression, classification, clustering, forecasting, and recommendations
- Design scalable and accurate predictive algorithms using advanced ML techniques
- Collaborate with engineering teams to productionize ML models via APIs and Docker
- Build and maintain CI/CD pipelines and MLOps workflows for continuous delivery
- Monitor model performance, detect drift, and implement retraining strategies
- Generate actionable insights to improve business outcomes
Qualifications
- Bachelor's degree or equivalent experience in Statistics, Mathematics, Computer Science, Engineering, or related quantitative field
- 7–12 years of experience in Machine Learning / Data Science roles
- Strong understanding of predictive modeling, ML algorithms, and statistical techniques
- Proficiency in Python and SQL with hands-on experience in NumPy, Pandas, and Scikit-learn
- Experience with ML algorithms such as XGBoost, Random Forest, SVM, Decision Trees, and ensemble methods
- Hands-on experience in ML model deployment, APIs, Docker, and CI/CD pipelines
- Familiarity with MLOps tools (MLflow / Kubeflow / Airflow) and cloud platforms (AWS / Azure / GCP)
- Knowledge of BI tools like Power BI or Tableau is a plus
