Data Scientist Machine Learning Development & Deployment

Job Description

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 outcomesQualifications:- 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 outcomesQualifications:- 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

Keyskills :
Machine LearningData SciencePythonSQLNumPySVMDecision TreesAPIsDockerBI ToolsPandasScikitlearnXGBoostRandom ForestEnsemble MethodsML Model DeploymentCICD PipelinesMLOps Tools