Data Scientist - Machine Learning/Artificial Intelligence

<p><p><b>Key Responsibilities :</b><br/><br/></p><p>- Design, develop, and deploy machine learning and AI models for predictive analytics and business solutions.<br/><br/></p><p>- Perform data preprocessing, feature engineering, and exploratory data analysis using Python, Pandas, and related tools.<br/><br/></p><p>- Evaluate, fine-tune, and optimize model performance using best practices in ML/AI.<br/><br/></p><p>- Build and maintain end-to-end data pipelines for seamless integration of large datasets.<br/><br/></p><p>- Collaborate with cross-functional teams (engineering, product, and business) to translate requirements into data-driven solutions.<br/><br/></p><p>- Implement and manage ML workflows on cloud platforms (Azure, AWS, GCP), ensuring scalability and efficiency.<br/><br/></p><p>- Communicate insights and results effectively using visualization tools and dashboards.<br/><br/></p><p>- Stay updated on emerging AI/ML trends, cloud technologies, and best practices to continuously improve Skills :</b></p><p><br/></p>- Machine Learning & Artificial Intelligence (AI)<br/><br/></p><p>- Predictive Modeling & Statistical Analysis<br/><br/></p><p>- Data Preprocessing & Feature Engineering<br/><br/></p><p>- Model Evaluation & Optimization<br/><br/></p><p>- Problem-Solving & Business Skills :</b></p><p><br/></p>- Programming : Python (expert), SQL (preferred)<br/><br/></p><p>- Libraries & Frameworks : Pandas, Scikit-learn, TensorFlow, Keras (optional : PyTorch)<br/><br/></p><p>- Cloud Platforms : Azure, AWS, GCP<br/><br/></p><p>- Data Visualization Tools : Matplotlib, Seaborn, Plotly, Tableau/Power BI<br/><br/></p><p>- Version Control & Collaboration : Git/GitHub<br/><br/></p><p>- Workflow & ML Lifecycle Tools : Airflow, MLflow & Experience :</b></p><p><br/></p>- Bachelors or Masters degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.<br/><br/></p><p>- 5+ years of experience in data science, including building and deploying ML/AI models in production environments.<br/><br/></p><p>- Hands-on experience with cloud-based ML pipelines and large-scale data / Success Metrics :</b></p><p><br/></p>- Accuracy, precision, recall, or other relevant performance metrics of deployed models.<br/><br/></p><p>- Time-to-deploy and reliability of ML pipelines.<br/><br/></p><p>- Business impact (e.g., revenue growth, cost reduction, operational efficiency).<br/><br/></p><p>- Number of actionable insights delivered to stakeholders.<br/><br/></p><p>- Model scalability, monitoring, and maintenance efficiency</p><br/></p> (ref:hirist.tech)