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Senior AI/ML Data Scientist I (P3)

Nielsen
Bengaluru, IND
Full-time
Senior · 5–9 years experience
Hybrid
Discovered Today
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Qualifications

  • Responsibilities: Model Development: Lead the development and implementation of data science solutions. Design, develop, train, and validate classical machine learning models (e.g., Regression, Classification, Clustering, Tree-based models like Random Forests, Gradient Boosting Machines, SVMs, etc.) to solve specific business problems. Data Preprocessing & Feature Engineering: Perform extensive data cleaning, transformation, and feature engineering to prepare diverse datasets for model training.

Responsibilities

  • Model Development: Lead the development and implementation of data science solutions. Design, develop, train, and validate classical machine learning models (e.g., Regression, Classification, Clustering, Tree-based models like Random Forests, Gradient Boosting Machines, SVMs, etc.) to solve specific business problems.
  • Data Preprocessing & Feature Engineering: Perform extensive data cleaning, transformation, and feature engineering to prepare diverse datasets for model training. Identify and create relevant features to improve model performance.
  • Exploratory Data Analysis (EDA): Conduct thorough EDA to understand data characteristics, identify patterns, anomalies, and relationships, and inform model selection and development.
  • Model Evaluation & Optimization: Implement rigorous model evaluation techniques (e.g., cross-validation, hyperparameter tuning) and metrics (e.g., accuracy, precision, recall, F1-score, ROC-AUC, RMSE, MAE) to assess model performance and optimize models for production.
  • Production Deployment (MLOps Fundamentals): Collaborate with MLOps/DevOps teams to integrate, deploy, and monitor classical ML models in production environments. Understand basic concepts of model serving and API development.
  • Algorithm Selection & Customization: Research and select appropriate classical ML algorithms based on problem type, data characteristics, and performance requirements.
  • Deep Learning and Neural Networks: Move beyond traditional ML algorithms to understand and implement deep learning architectures (CNNs, LSTMs, Transformers) for tasks like image recognition, natural language processing, and sequence modeling.
  • Documentation & Communication: Document models, methodologies, and results clearly and concisely. Effectively communicate complex technical concepts to both technical and non-technical stakeholders.
  • Research & Innovation: Stay updated with the latest advancements in classical machine learning, statistical modeling, and data science best practices. Mentor junior data scientists and contribute to a culture of continuous learning and improvement.
  • Collaboration: Collaborate with cross-functional teams to define project requirements and deliver impactful results. Work closely with data scientists, data engineers, product managers, and business analysts to define problems, gather requirements, and deliver impactful ML solutions.

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