Key Responsibilities

- Model Development & Engineering: Design, train, fine-tune, and deploy predictive ML models and algorithms to address domain problems (e.g.,

churn forecasting, recommendation systems, portfolio analytics, anomaly detection).
- NLP Implementation: Develop and maintain NLP pipelines to parse earnings reports, news articles, financial disclosures, and customer interaction logs.
- MLOps & Productionization: Implement scalable ML pipelines, RESTful

APIs, and MLOps practices for seamless model deployment, continuous monitoring, and automated retraining.
- Data Analytics & Feature Engineering: Perform exploratory data analysis

(EDA) on structured market data and unstructured platform data to craft high-impact features.
- Data Pipelines: Collaborate with Data Engineers to build scalable data pipelines handling streaming tick data, transactional logs, and reference data.

Required Qualifications & Skills
- Experience: 35 years of experience as a Data Scientist,



ML Engineer, or AI

Engineer in a fast-paced environment (Stock Broking, Fintech, or E-

commerce experience is a plus).

- Core Skills: Strong proficiency in Python, SQL, and Machine Learning libraries (Scikit-Learn, PyTorch, TensorFlow, XGBoost).
- NLP Expertise: Hands-on experience with contemporary NLP frameworks

(Hugging Face, Transformers, spaCy, NLTK) and LLM application frameworks (LangChain, LlamaIndex).
- MLOps & Cloud: Experience with Docker, Kubernetes, CI/CD for ML, cloud environments (AWS/GCP/Azure), and feature stores.
- Data Analytics: Proficiency in handling high-volume datasets using SQL,

Pandas, PySpark, and visualization libraries.
- Education: Bachelor’s or Master’s degree in Computer Science, Data

Science, Engineering, Statistics,

📌 Data Science Engineer (Mumbai)
🏢 Spectrum
📍 Mumbai