Data Scientist - Artificial Intelligence/Machine Learning

Role Overview :

You will collaborate with cross-functional teams including product, engineering, and domain experts to design scalable pipelines, drive business insights, and deliver innovative AI-powered solutions that create measurable business impact.

Key Responsibilities :

- Apply advanced neural architectures such as CNNs, RNNs, LSTMs, Transformers, and Autoencoders to solve real-world business problems.

- Implement Natural Language Processing (NLP) techniques, including BERT, GPT-based models, embeddings, semantic similarity, and topic modeling.

- Build and maintain predictive analytics pipelines for industrial/manufacturing use cases, especially predictive maintenance.

- Conduct Exploratory Data Analysis (EDA), feature engineering, data preprocessing, and statistical validation to ensure robust model performance.

- Collaborate with data engineers to design scalable data pipelines and ensure seamless integration of ML models into production systems.

- Communicate findings and insights to stakeholders through clear visualizations, dashboards, and reports.

- Stay updated with the latest research and advancements in AI/ML, and recommend adoption of new methods where relevant.

Required Skills & Qualifications :

- Programming & Tools : Strong proficiency in Python and ML libraries (Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, Keras).

- Experience : 34 years of hands-on experience in AI/ML and Deep Learning model development, deployment, and optimization.

- Deep Learning Expertise : Solid knowledge of CNNs, RNNs, LSTMs, Transformer-based architectures.

- NLP Knowledge : Practical experience with modern NLP models (e.g., BERT, GPT, embeddings, text classification, sentiment analysis).

- Statistical Foundation : Strong background in probability, hypothesis testing, statistical inference, and mathematical modeling.

- Database & Data Handling : Experience with SQL and databases (PostgreSQL, InfluxDB, or similar) for data extraction, transformation, and analysis.

- Problem-Solving Mindset : Ability to translate complex business challenges into data-driven solutions.

Preferred Skills (Nice to Have) :

- Experience in MLOps frameworks (MLflow, Kubeflow, Airflow).

- Exposure to big data tools (Spark, Hadoop).

- Prior work in industrial/manufacturing analytics or IoT data.

- Knowledge of data visualization tools (Power BI, Tableau, Plotly).