About the role

AI summarised

The Staff Engineer Data Scientist will lead the development of data analytics and AI capabilities, focusing on building and maintaining data pipelines, developing predictive and deep learning models, and communicating complex findings to technical and business audiences. The role requires strong programming skills in Python and SQL, experience with machine learning libraries, and a background in data science or related fields. The ideal candidate will have 3-5 years of relevant experience and a passion for clean, well-documented code and cross-functional collaboration.

IDMOnsiteBE

Key Responsibilities

Growing the Organization’s data analytics and AI capability
Develop and maintain a data pipeline for large-scale data indexing
Engage in both exploratory analysis and predictive models to identify data trends and anomalies
Explore new hypotheses, build deep learning algorithms and be responsible to maintain model quality over time
Take ownership of the algorithmic structure and explain complex deep learning algorithms in layman terms to business stakeholders or tech talks
Document analytical findings for technical teams, executives, or publication

Requirements

Ph.D. or Masters in Natural Science, Computer Science, Data Science, Statistics, Mathematics, or equivalent fields
At least 3-5 years of relevant working experience in similar fields
Proficient in Python and SQL languages
Ability to handle text manipulation tasks such as processing and parsing
Understanding of recommender systems such as collaborative filtering and content filtering
Experience in using machine learning libraries such as Scikit-learn, TensorFlow, Keras or PyTorch
Experience in model hyper-parameter tuning, embeddings and feature engineering
Experience in natural language process (NLP), statistical modeling, network analysis, and data mining techniques
Love minimal, beautiful code and neat documentation
Team player, both internal between data scientist as external with business stakeholders
Experience with cloud computing (AWS/GCP/Azure)
Deep Learning (RNN, LSTM, XGBoost)
Continuous deployment (CI/CD)