Machine Learning Developer - Bangalore
Bangalore, India
Nielsen
A global leader in audience insights, data and analytics, Nielsen shapes the future of media with accurate measurement of what people listen to and watch.Responsibilities:
- Research, design, develop, implement and test econometric, statistical, optimization and machine learning models.
- Design, write and test modules for Nielsen analytics platforms using Python, R, SQL and/or Spark.
- Utilize advanced computational/statistics libraries including Spark MLlib, Scikit-learn, SciPy, StatsModels or R.
- Collaborate with cross functional Data Science, Product, and Technology teams to integrate best practices from across the organization
- Provide leadership and guidance for the team in the of adoption of new tools and technologies to improve our core capabilities
- Execute and refine the roadmap to upgrade the modeling/forecasting/control functions of the team to improve upon the core service KPI’s
- Ensure product quality, stability, and scalability by facilitating code reviews and driving best practices like modular code, unit tests, and incorporating CI/CD workflows
- Explain complex data science (e.g. model-related) concepts in simple terms to non-technical internal and external audiences
Key Skills:
- 5+ years of professional work experience in Statistics, Data Science, and/or related disciplines, with focus on delivering analytics software solutions in a production environment
- Strong programming skills in Python with experience in NumPy, Pandas, SciPy and Scikit-learn.
- Hands-on experience with deep learning frameworks (PyTorch, TensorFlow, Keras).
- Solid understanding of Machine learning domains such as Computer Vision, Natural Language Processing and classical Machine Learning.
- Proficiency in SQL and NoSQL databases for large-scale data manipulation
- Experience with cloud-based ML services (AWS SageMaker, Databricks, GCP AI, Azure ML).
- Knowledge of model deployment (FastAPI, Flask, TensorRT, ONNX) MLOps tools (MLflow, Kubeflow, Airflow) and containerization.
Preferred skills:
- Understanding of LLM fine-tuning, tokenization, embeddings, and multimodal learning.
- Familiarity with vector databases (FAISS, Pinecone) and retrieval-augmented generation (RAG).
- Familiarity with advertising intelligence, recommender systems, and ranking models.
- Knowledge of CI/CD for ML workflows, and software development best practices.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: Airflow AWS Azure CI/CD Computer Vision Databricks Deep Learning FAISS FastAPI Flask GCP Keras KPIs Kubeflow LLMs Machine Learning MLFlow ML models MLOps Model deployment NLP NoSQL NumPy ONNX Pandas Pinecone Python PyTorch R RAG Recommender systems Research SageMaker Scikit-learn SciPy Spark SQL Statistics statsmodels TensorFlow TensorRT
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