What are the responsibilities and job description for the Data Science Architect position at iPeople Infosystems LLC?
Job Details
Job Title: Data Science Architect
Location: NJ/NYC, NY /Dallas, TX/Charlotte, NC/Atlanta, GA/Chicago, IL (Onsite)
Type: Fulltime Position
Job Overview:
- Proficient in Python, scikit-learn, TensorFlow, PyTorch, HuggingFace.
- Strong BFSI domain knowledge.
- Experience with NLP, LLMs (GPT), and deep learning.
- Hands-on with MLOps pipelines and tools.
- Experience with graph analytics tools (Neo4j, TigerGraph, NetworkX).
- Strong cloud architecture experience in AWS, Google Cloud Platform, or Azure.
Roles & Responsibilities:
An experienced Data Science Architect to lead end-to-end AI/ML solution design and implementation across a range of business domains in financial services. You will be responsible for architecting robust, scalable, and secure data science solutions that drive innovation and competitive advantage in the BFSI sector. This includes selecting appropriate technologies, defining solution blueprints, ensuring production readiness, and mentoring cross-functional teams. You will work closely with stakeholders to identify high-value use cases and ensure seamless integration of models into business applications. Your deep expertise in machine learning, cloud-native architectures, MLOps practices, and financial domain knowledge will be essential to influence strategy and deliver transformative business impact.
- Architect and drive the design, development, and deployment of scalable ML/AI solutions.
- Lead data science teams through complete project lifecycles from ideation to production.
- Define standards, best practices, and governance for AI/ML solutioning and model management.
- Collaborate with data engineering, MLOps, product, and business teams.
- Oversee integration of data science models into production systems.
- Evaluate and recommend ML tools, frameworks, and cloud-native solutions.
- Guide feature engineering, data strategy, and feature store design.
- Promote innovation with generative AI, reinforcement learning, and graph-based learning.
- Knowledge of Spark, PySpark, Scala.
- Experience leading CoEs or data science accelerators.
- Open-source contributions or published research.
