SONY Internship 2026
SONY
Key Responsibilities
- As a Data Science Intern at Sony Research India, you will work directly on advancing predictive algorithms, multi-modal architectures, and video analysis pipelines. You will move fluidly between empirical research and production software execution.
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- Analyze Massive Datasets: Mine, clean, and analyze high-dimensional user interaction data, identifying latent behavioral structures and converting noisy data signals into feature spaces.
- Data Annotations & Insights: Evaluate data annotations and translate complex analytical findings into actionable technical recommendations.
- Implement Cutting-Edge Algorithms: Design, build, and optimize state-of-the-art AI/ML models including Large Language Models (LLMs) and video analysis algorithms.
- Cross-Functional Collaboration: Act as technical tissue connecting core engineering divisions with strategic business units, aligning technical model outputs with business KPIs.
- Scale & Deploy: Write production-ready, clean, modular Python code to scale algorithms and pipeline architectures in modern cloud environments.
- Experimentation & Evaluation: Formulate offline verification frameworks and contribute to online A/B testing methodologies to assess model accuracy and efficiency.
- Analyze Massive Datasets: Mine, clean, and analyze high-dimensional user interaction data, identifying latent behavioral structures and converting noisy data signals into feature spaces.
- Data Annotations & Insights: Evaluate data annotations and translate complex analytical findings into actionable technical recommendations.
- Implement Cutting-Edge Algorithms: Design, build, and optimize state-of-the-art AI/ML models including Large Language Models (LLMs) and video analysis algorithms.
- Cross-Functional Collaboration: Act as technical tissue connecting core engineering divisions with strategic business units, aligning technical model outputs with business KPIs.
- Scale & Deploy: Write production-ready, clean, modular Python code to scale algorithms and pipeline architectures in modern cloud environments.
- Experimentation & Evaluation: Formulate offline verification frameworks and contribute to online A/B testing methodologies to assess model accuracy and efficiency.
Skills & Eligibility
- Currently pursuing or completed B.E / B.Tech, M.E / M.Tech, or MS degrees in Computer Science, Data Science, Statistics, Mathematics, or parallel quantitative disciplines.
- Demonstrable expertise in AI/ML paradigms, specifically Video Analysis algorithms and Large Language Models (LLMs).
- Strong theoretical and practical grasp of supervised/unsupervised learning, custom loss functions, and model bias mitigation techniques.
- Advanced proficiency in Python and foundational framework libraries: PyTorch, TensorFlow, Jax, and Scikit-Learn.
- Experience handling large datasets using distributed computing engines such as SQL, PySpark, Polars, or Apache Arrow.
- Familiarity with production MLOps pipelines and cloud deployment services.
- Currently pursuing or completed B.E / B.Tech, M.E / M.Tech, or MS degrees in Computer Science, Data Science, Statistics, Mathematics, or parallel quantitative disciplines.
- Demonstrable expertise in AI/ML paradigms, specifically Video Analysis algorithms and Large Language Models (LLMs).
- Strong theoretical and practical grasp of supervised/unsupervised learning, custom loss functions, and model bias mitigation techniques.
- Advanced proficiency in Python and foundational framework libraries: PyTorch, TensorFlow, Jax, and Scikit-Learn.
- Experience handling large datasets using distributed computing engines such as SQL, PySpark, Polars, or Apache Arrow.
- Familiarity with production MLOps pipelines and cloud deployment services.
Note: This job is posted on external sites. Joblit shares the listing for convenience and does not take responsibility for third-party content.
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