As a Data Scientist at our company, you will be responsible for analyzing and preprocessing raw data, developing machine learning models, and designing accurate predictive algorithms using advanced ML techniques. Your collaboration with engineering teams to productionize ML models, build CI/CD pipelines, and monitor model performance will be crucial in driving data-driven decision making. Additionally, you will generate actionable insights to improve business outcomes. Key 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:
Bachelors degree or equivalent experience in Statistics, Mathematics, Computer Science, Engineering, or related quantitative field
712 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 As a Data Scientist at our company, you will be responsible for analyzing and preprocessing raw data, developing machine learning models, and designing accurate predictive algorithms using advanced ML techniques. Your collaboration with engineering teams to productionize ML models, build CI/CD pipelines, and monitor model performance will be crucial in driving data-driven decision making. Additionally, you will generate actionable insights to improve business outcomes. Key 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:
Bachelors degree or equivalent experience in Statistics, Mathematics, Computer Science, Engineering, or related quantitative field
712 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
