Role Overview:We are looking for a Data Scientist / AI-ML Engineer with solid experience in statistical modeling, machine learning, data analysis, and predictive analytics. The candidate will develop, evaluate, and deploy machine learning models to solve business problems, improve decision-making, and deliver measurable business outcomes.Key Responsibilities:- Analyze structured and unstructured datasets to identify patterns, trends, and actionable insights.- Develop, train, validate, and optimize machine learning models for classification, regression, clustering, forecasting, and anomaly detection.- Apply statistical analysis, hypothesis testing, feature engineering, and exploratory data analysis.- Build predictive models and translate business requirements into data science solutions.- Develop data preprocessing and transformation pipelines for model training and evaluation.- Evaluate model performance using appropriate metrics and validation techniques.- Implement machine learning solutions using Python and relevant data science libraries.- Collaborate with data engineers, software developers, product managers, and business stakeholders to operationalize models.- Support deployment of models into production environments and monitor their performance.- Identify model drift, data quality issues, and performance degradation, and implement corrective measures.- Document model assumptions, experiments, methodologies, and results.- Follow best practices for reproducibility, version control, data governance,



and responsible AI.Required Skills:- Strong proficiency in Python for data analysis and machine learning.- Experience with libraries such as pandas, NumPy, scikit-learn, and SciPy.- Strong understanding of statistics, probability, linear algebra, and machine learning algorithms.- Hands-on experience with supervised and unsupervised learning techniques.- Experience with feature engineering, model selection, hyperparameter tuning, and model evaluation.- Working knowledge of SQL and data extraction from relational databases.- Familiarity with data visualization tools such as Matplotlib, Seaborn, or Plotly.- Understanding of data cleaning, missing-value treatment, outlier detection, and feature selection.- Exposure to deep learning frameworks such as PyTorch or TensorFlow is desirable.- Familiarity with Git, Jupyter notebooks, and collaborative development practices.- Basic understanding of model deployment, APIs, and production machine learning workflows.Preferred Qualifications:- Exposure to NLP, computer vision, recommendation systems, or time-series forecasting.- Familiarity with cloud-based machine learning services on AWS, Azure, or GCP.- Exposure to MLflow, experiment tracking, model versioning, or similar tools.- Understanding of model explainability, bias assessment, and responsible AI practices.Key Competencies:- Analytical thinking, statistical reasoning, experimentation, problem-solving, effective communication, and the ability to translate data into practical business solutions. (ref:hirist.tech)

📌 Data Scientist Ml Ai Python (Bengaluru)
🏢 Edgeverve Systems
📍 Bengaluru