Senior Data Scientist

Responsibilities:

• Perform Data cleansing activities such as data cleaning, merging enrichment, etc.
• Perform feature engineering by extracting meaningful features from measured and/or derived data.
• Perform exploratory and targeted data analyses to get key insights.
• Build Stochastic and Machine-learning algorithms that potentially address business problems.
• Lead and implemented Machine Learning projects from initiation through completion with a particular focus on automated deployment and ensuring optimized performance.
• Maintain and optimize the machine learning/deep learning models developed by the Data Scientist and ensure seamless deployment in different environments (Dev/QA/Prod) while enabling model tracking, model experimentation, and model automation.
• Collaborate with the data engineers and data scientists on model development to containerize and build out the deployment pipelines for new models.
• Collaborate on MLOPS life cycle and experience with MLOPS workflows and traceability and versioning of datasets.
• Ensure traceability and versioning of datasets, models, and evaluation pipelines.
• Design, prototype, build, and maintain APIs for consumption of machine learning models at scale.
• Facilitate the development and deployment of POC machine-learning systems.
• Using standard methodologies framework to ensure data quality and reconciliation checks are in place and are transparent to everyone.

Requirements:

• Strong understanding of advanced data mining techniques, curating, processing, and transforming data to produce sound datasets.
• Strong understanding of the Machine Learning lifecycle - feature engineering, training, validation, scaling, deployment, scoring, monitoring, and feedback loop.
• Experience in analyzing complex problems and translating them into an analytical approach.
• Experience in Supervised and Unsupervised Machine Learning including Classification, Forecasting, Anomaly Detection, Pattern Detection, and Text Mining, using a variety of techniques such as Decision trees, Time Series Analysis, Bagging and Boosting algorithms, Neural Networks, and Deep Learning.
• Experience with analytical programming languages, tools, and libraries (Python ecosystem preferred, but R will be considered).
• Has used frameworks/libraries such as Scikit-learn, PyTorch, Keras, and NLTK.
• Deep understanding of data structures and algorithms.
• Excellent programming skills in Python and PySpark.
• Hands-on experience using Spark (in Python) - to develop high-volume ETL pipelines.
• Have implemented machine learning projects from initiation through completion with a particular focus on automated deployment and ensuring optimized performance.
• Have maintained and optimized the machine learning/deep learning models developed by the Data Scientist and ensured seamless deployment in different environments while enabling model tracking, model experimentation, and model automation.
• Experience in SQL and relational databases, Big Data technologies e. g., Spark/Hadoop and Cloud technologies.
• Hands-on experience with workflow management tools such as Airflow.
• A strong understanding of RESTful APIs and/or data streaming is a big plus.
• Required experience in modern version control (GitHub, Bitbucket).
• Hands-on experience with containerization (Docker, Kubernetes, etc. ).
• At least 5+ years of professional experience as a data scientist or ML Engineer.

Educational Qualifications:

• Advanced degree in an analytical field (e. g., Data Science, Computer Science, Engineering, Applied Mathematics, Statistics, Data Analysis, Operations Research, Artificial Intelligence, or Applied Math).