Key Responsibilities:

<ul>
<li>Lead end-to-end development of machine learning and statistical models — from data exploration and feature engineering to training, validation, and deployment.</li>
</ul>

<ul>
<li>Collaborate with cross-functional teams (Product, Engineering, Business, and AI) to identify opportunities for data science solutions.</li>
</ul>

<ul>
<li>Apply advanced techniques in predictive modeling, NLP, computer vision, and deep learning to solve complex business problems.</li>
</ul>

<ul>
<li>Implement Generative AI, LLMs, and LangChain frameworks for intelligent automation, content generation, and conversational AI systems.</li>
</ul>

<ul>
<li>Build and optimize ETL data pipelines using Python, SQL, and cloud services (AWS, GCP, or Azure).</li>
</ul>

<ul>
<li>Perform A/B testing, model evaluation, and post-deployment monitoring to ensure accuracy and stability.</li>
</ul>

<ul>
<li>Visualize insights and communicate findings to stakeholders using dashboards and storytelling tools (e.g., Power BI, Tableau, Plotly).</li>
</ul>

<ul>
<li>Mentor junior data scientists and contribute to establishing best practices in model development, versioning, and MLOps.</li>
</ul>

Required Skills &amp; Technologies:

<ul>
<li>Programming: Python, R, SQL, PySpark</li>
</ul>

<ul>
<li>ML/DL Frameworks: Scikit-learn, TensorFlow, PyTorch, Keras</li>
</ul>

<ul>
<li>NLP / GenAI: Hugging Face, LangChain, OpenAI APIs, Prompt Engineering</li>
</ul>

<ul>
<li>Cloud Platforms: AWS (SageMaker, Bedrock), Azure ML, GCP AI Platform</li>
</ul>

<ul>
<li>Data Tools: Pandas, NumPy, Airflow, Kafka, Snowflake, Databricks</li>
</ul>

<ul>
<li>Visualization: Power BI, Tableau, Seaborn, Matplotlib, Plotly</li>
</ul>

<ul>
<li>MLOps: MLflow, Docker, Kubernetes, Git, CI/CD pipelines</li>
</ul>

<ul>
<li>Strong foundation in statistics, probability, linear algebra, and optimization</li>
</ul>

Preferred Qualifications:

<ul>
<li>Master’s or Ph.D. in Computer Science, Data Science, Statistics, or a related field</li>
</ul>

<ul>
<li>Proven experience leading data-driven projects end-to-end in a production environment</li>
</ul>

<ul>
<li>Experience with LLM fine-tuning, retrieval-augmented generation (RAG), or semantic search</li>
</ul>

<ul>
<li>Excellent communication skills and ability to translate technical findings into actionable business insights</li>
</ul>

Data Scientist

India

Key Responsibilities: Lead end-to-end development of machine learning and statistical models — from data exploration and feature engineering to training, validation, and deployment. Collaborate with cross-functional teams (Product, Engineering, Business, and AI) to identify opportunities for data science solutions. Apply advanced techniques in predictive modeling, NLP, computer vision, and deep learning to solve complex business problems. Implement Generative AI, LLMs, and LangChain frameworks for intelligent automation, content generation, and conversational AI systems. Build and optimize ETL data pipelines using Python, SQL, and cloud services (AWS, GCP, or Azure). Perform A/B testing, model evaluation, and post-deployment monitoring to ensure accuracy and stability. Visualize insights and communicate findings to stakeholders using dashboards and storytelling tools (e.g., Power BI, Tableau, Plotly). Mentor junior data scientists and contribute to establishing best practices in model development, versioning, and MLOps. Required Skills &amp; Technologies: Programming: Python, R, SQL, PySpark ML/DL Frameworks: Scikit-learn, TensorFlow, PyTorch, Keras NLP / GenAI: Hugging Face, LangChain, OpenAI APIs, Prompt Engineering Cloud Platforms: AWS (SageMaker, Bedrock), Azure ML, GCP AI Platform Data Tools: Pandas, NumPy, Airflow, Kafka, Snowflake, Databricks Visualization: Power BI, Tableau, Seaborn, Matplotlib, Plotly MLOps: MLflow, Docker, Kubernetes, Git, CI/CD pipelines Strong foundation in statistics, probability, linear algebra, and optimization Preferred Qualifications: Master’s or Ph.D. in Computer Science, Data Science, Statistics, or a related field Proven experience leading data-driven projects end-to-end in a production environment Experience with LLM fine-tuning, retrieval-augmented generation (RAG), or semantic search Excellent communication skills and ability to translate technical findings into actionable business insights

Data Scientist · Apollo

Job highlights

Skillset required

Job Description

Job Description for Data Scientist

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