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Data Scientist - Machine Learning / Artificial Intelligence

Data Scientist - Machine Learning / Artificial Intelligence

Right Move Staffing Solutions Private LimitedPune
14 days ago
Job description

Key Responsibilities :

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

(ref : hirist.tech)

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Data Scientist • Pune