Senior Quantitative Analytics Specialist

About this role:

The Enterprise Data Science (EDS) organization is looking for an established and proven Data Engineering expert to join our team and help us build scalable model ready data infrastructure through end-to-end development and ownership of data ingestion, data pipelines and model deployment frameworks. Incumbent is expected to lead Data Engineering best practices and automation and build scalable and reliable data pipelines and frameworks (from varied data sources) in collaboration with Data Science and platforms teams (in both batch and streaming environments). Additionally incumbent will be working with Data management and governance teams to create and maintain pristine quality of data in line with best practices and regulatory requirements, for faster adoption into AI/ML products and solutions.

In this role, you will:

• Design, develop, and deliver large-scale data ingestion, data processing, and data transformation projects, from various structured and unstructured data sources, supporting on-prem and cloud deployments
• Work closely with business partners, data scientists, technology teams and ML engineers to create the most suitable and scaled data engineering solutions, feature stores, etc.
• Automate data ingestion, pipelines and feature creation processes across tabular, semi-structured, free text, voice and image data. Figure out efficient ways to transform and store for scaled modeling usage
• As a senior member of the Data engineering team, contribute to development and scaling of end-to-end Data engineering team and capability. Guide junior engineers to build best-in class frameworks. Work with data science, ML engineers and technology partners and chalk out the roadmap and implementation strategy
• Guide data scientists to adopt data ingestion best practices during model exploration, development and deployment. Get involved in early scoping phases of projects/products and provide thought leadership on the right pipeline architecture
• Contribute to cloud migration strategy for data engineering and ML Ops solutions. Migrate data infrastructure from on-prem to private and public cloud (GCP)
• Keep up with emerging best practices in data engineering and drive adoption as necessary.
• Advocates for and ensures their team adheres to software engineering best practices (e.g. technical design and review, unit testing, monitoring, alerting, checking in code, code review)

Required Qualifications:

• B.S/B.Tech/B.E. degree or higher in a quantitative field such as computer sciences, applied math, statistics, engineering
• 6-10 years of experience in relevant fields like Data engineering, data warehousing, data lakes, ETL/ELT covering data solutions architecture design and implementation
• 4+ years advanced programming experience in Python, Spark, SQL, Scala, SAS (expert level proficiency)
• 4+ years of experience in big data stack like Hadoop, Hive, Kafka, Impala (expert level proficiency)
• 4+ years of experience across SQL databases like Teradata, Oracle and NoSQL databases like MongoDB, Cassandra. Experience of graph databases is a bonus
• 2+ years of experience in ML workflow technology like Airflow, Kubeflow
• Experience with implementing CI/CD principles and version control in the Machine Learning domain
• Exposure to tools like Databricks/Dataiku
• Experience creating data pipelines and ML Ops environment on Cloud (GCP, AWS, Azure) (GCP - preferred). Hands on experience on migrating data infrastructure from on-prem to GCP will be a bonus. BigQuery, Cloud Composer, Vertex AI
• Ability to interact with both business and technology partners on tech migration/adoption
• Takes ownership for responsibilities for own and drive same effort to the team
• Dedicated, enthusiastic, driven and performance-oriented; possesses a strong work ethic and good team player

Desired Qualifications:

• Experience in Agile development methods
• Familiarity with AI/ML modeling frameworks like Scikit-learn, SparkML, TensorFlow, PyTorch, Keras
• Familiarity with AI/ML and NLP modeling techniques like Random forest, XGboost, Deep learning, Topic modeling, Text analytics
• Experience in banking and BFSI, retail, e-commerce, product companies (preferred)
• Experience in deployment through containers (like Docker) and orchestration (Kubernetes)
• Experience in deploying Machine Learning as-a-service using REST API’s, Flask, Django, etc.
• Experience building custom integrations between cloud-based systems using APIs
• Experience with elastic search, knowledge graph
• Experience with ML model testing: model performance, model health, etc.

Job Expectations:

• As mentioned above

@RWF22

Posting End Date: 

*Job posting may come down early due to volume of applicants.

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