In this role, you will be responsible for developing data-driven solutions, building advanced machine learning and deep learning models, and leading the design of experiments and statistical analysis.

You will also be expected to contribute to applied research in Generative AI, time-series forecasting, and computer vision use cases.
This is an exciting opportunity to influence product decisions and innovation using data and AI.

Key Responsibilities :

- Work on end-to-end data science projects from data exploration, feature engineering, and model development to deployment and monitoring.

- Perform data wrangling, cleaning, and transformation using tools like Pandas, NumPy, and SQL (MySQL, PostgresDB).

- Build and evaluate statistical models and machine learning algorithms, ensuring robustness and scalability.

- Apply time series forecasting techniques such as ARIMA, Prophet, and seasonal decomposition for business-critical applications.

- Develop and fine-tune tree-based models like Random Forest, Gradient Boosting, XGBoost, or LightGBM.

- Design and train deep learning models, particularly CNNs, ResNets, and UNets, for structured and unstructured data (e.g., images, text).

- Utilize Generative AI models like GPT-4, Mistral, Claude, or LLaMA, including prompt engineering, API integration, and custom fine-tuning.

- Create interactive visualizations and dashboards using Matplotlib, Plotly, Altair, or Dash to communicate insights to stakeholders.

- Collaborate cross-functionally with data engineers, product teams, and software developers to integrate models into production systems.

- Follow best practices in code versioning (Git/GitHub), code reviews, and Agile workflows (e.g., Scrum, Kanban).

- Stay updated with the latest research and trends in machine learning, deep learning, and GenAI.

Requirements & Qualifications :

- Bachelor's or Masters degree in Computer Science, Statistics, Mathematics, Data Science, or a

related field.

- 5+ years of hands-on experience in data science, with a proven track record of delivering

production-ready models.

- Strong skills in Python for data analysis and modeling with proficiency in libraries such as Pandas, NumPy, SciPy, Statsmodels, Scikit-learn, and PyTorch or TensorFlow.

- Proficiency in querying relational databases (MySQL, Postgres) and data manipulation.

- Experience working with statistical tests, PCA, correlation analysis, and feature selection techniques.

- Proficient in data visualization using Matplotlib, Plotly, or similar frameworks to generate actionable insights.

- Solid understanding of and experience with time series forecasting techniques and models.

- Applied experience with tree-based machine learning models and model interpretation tools (e.g., SHAP, LIME).

- Experience with deep learning architectures (especially CNNs and variants like ResNet, UNet) for tasks like classification, segmentation, or object detection.

- Hands-on experience in using and customizing Large Language Models (LLMs) such as GPT-4,

LLaMA, Mistral, or Claude.
- Experience with prompt engineering, fine-tuning, and deployment of GenAI-based solutions using OpenAI, Hugging Face, or similar frameworks.
- Comfortable with version control and collaboration tools like Git, GitHub, and Jupyter Notebooks