Data Science (Senior Software Engineer)

Job Title: Data Science
Position: Senior Software Engineer
Experience: 7 - 12 Years
Category: Software Development/ Engineering
Shift: Hybrid - (1PM to 10 PM Shift)
Main location: Hyderabad
Employment Type: Full Time

Must-have skills

Python, Machine Learning, Deep Learning, Generative AI, LLM, NLP, Statistics, PyTorch or TensorFlow, Scikit-learn, NumPy, Pandas, Prompt Engineering, Embeddings, LLM Fine-tuning.

Preferred / Additional skills

CNN, RNN, LSTM, Transformers, Regression, A/B Testing, Hypothesis Testing, MLflow, Airflow, Kubeflow, AWS/Azure/GCP, Docker, Kubernetes, Git, GANs, VAEs, Diffusion Models.

Education Qualification: Bachelor's degree in Computer Science or related field or higher with minimum 6 years of relevant experience.
Position Description: Were looking for a Senior Data Scientist with strong analytical thinking, deep technical expertise, and a proven ability to apply machine learning to solve real-world problems. The candidate should have 4+ years of high-quality proven experience in AI/ML working. Youll work with cross-functional teams to design, develop, and deploy scalable AI/ML solutions, including Generative AI, predictive modeling, and recommendation systems.

Your future duties and responsibilities
Design, build, and validate machine learning and deep learning models, ensuring robustness, scalability, and explainability.
• Apply strong statistical foundations to analyze large datasets and derive actionable insights.
• Lead the development and evaluation of models using modern ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow).
• Drive the adoption of Generative AI and LLM-based solutions, ensuring model alignment, prompt engineering, and ethical AI practices.
• Collaborate with data engineers, product teams, and business stakeholders to transform business problems into technical solutions.
• Contribute to code reviews, model documentation, and mentorship of junior data scientists.
Stay abreast of the latest research and translate cutting-edge methods into production.

• Statistical and Mathematical Rigor
  o Strong grasp of descriptive and inferential statistics (hypothesis testing, A/B testing, regression, probability theory).
  o Understanding of bias-variance trade-off, regularization, overfitting, and model validation techniques.
  
• Machine Learning & Deep Learning
  o Hands-on experience with a range of algorithms: decision trees, ensemble models, SVMs, neural networks, clustering, and NLP techniques.
  o Proficiency in deep learning architectures such as CNNs, RNNs, Transformers, and LSTMs.
  
• Generative AI & LLMs
  o Conceptual and practical knowledge of Large Language Models (e.g., GPT, BERT, LLaMA), fine-tuning, embeddings, and prompt engineering.
  o Familiarity with generative modeling approaches (e.g., VAEs, GANs, diffusion models) is a strong plus.
  
• Programming & Problem Solving
  o Advanced proficiency in Python with the ability to write clean, modular, and testable code.
  o Experience with libraries such as NumPy, pandas, matplotlib, scikit-learn, PyTorch, TensorFlow, and HuggingFace.
  o Strong problem-solving skills with the ability to tackle coding challenges independently and efficiently.
  
• Tooling & Deployment
  o Experience with cloud platforms (AWS, GCP, Azure), ML pipelines (MLflow, Airflow, Kubeflow), and containerization (Docker, Kubernetes).
  o Version control (Git) and collaborative development practices.
  

Required qualifications to be successful in this role
Bachelors or masters degree in computer science, Statistics, Applied Mathematics, or a related field.

Statistical and Mathematical Rigor
o Strong grasp of descriptive and inferential statistics (hypothesis testing, A/B testing, regression, probability theory).
o Understanding of bias-variance trade-off, regularization, overfitting, and model validation techniques.

Machine Learning & Deep Learning
o Hands-on experience with a range of algorithms: decision trees, ensemble models, SVMs, neural networks, clustering, and NLP techniques.
o Proficiency in deep learning architectures such as CNNs, RNNs, Transformers, and LSTMs.

Generative AI & LLMs
o Conceptual and practical knowledge of Large Language Models (e.g., GPT, BERT, LLaMA), fine-tuning, embeddings, and prompt engineering.
o Familiarity with generative modeling approaches (e.g., VAEs, GANs, diffusion models) is a strong plus.

Programming & Problem Solving
o Advanced proficiency in Python with the ability to write clean, modular, and testable code.
o Experience with libraries such as NumPy, pandas, matplotlib, scikit-learn, PyTorch, TensorFlow, and HuggingFace.
o Strong problem-solving skills with the ability to tackle coding challenges independently and efficiently.

Tooling & Deployment