Applied Data Scientist (GenAI)

Infosys•Raleigh, AZ

2d•Hybrid

About The Position

In the assigned Job Role of Data Science Consultant 2, your Area Of Responsibility will be as below: Develop data preparation tasks, while identifying patterns or anomalies. Ensure data readiness for advanced modeling. Develop models for complex use cases (e.g., forecasting models, LLM-based solutions), while refining algorithms to meet business needs, and ensure smooth deployment into scalable, production-ready solutions. Conduct testing and optimize algorithms for performance, reliability, and scalability, while providing guidance to team members in best practices. Design and develop predictive models and data-driven analyses to address business challenges. Build, evaluate, and deploy models, standardize code, and contribute to knowledge management. Leverage tools like SAS and R/Python to create reusable customizations for non-ML, ML, and deep learning algorithms, while enhancing analytics including LLMs, and create innovative, cost-effective solutions. Define analytics problems for projects; execute visualization, analysis, and predictive modeling under guidance. Proactively maintain models and implement improvements for accuracy and reliability. Apply governance controls to mitigate risks and ensure compliance. Analyze performance trends, recommend improvements, and document discrepancies for escalation. Maintain comprehensive documentation standards, while participating in knowledge transfer sessions. Participate in discussions with stakeholders to refine requirements, provide insights, and guide implementation of models. Apply the predefined quality measurement framework at an individual task level in the project. Deploy complex analytics tools or multi-system integration, while validating deployment success. Participate in developing scripts or templates for repeated deployments tasks. Contribute to analytic solutions, IP asset creation, and training initiatives. Contribute to thought leadership such as papers, innovative non-ML, ML, deep learning or LLM models, and proofs of concepts. Participate in and deliver analytics training, while contributing to content creation. Provide input for segment and unit-level business plans. Your contribution to the team: Deliver scalable, high-quality analytics solutions aligned to business needs. A knack for optimization, deployment and performance improvement of models. The ability to drive innovation through advanced analytics, automation and thought leadership. Enable team growth through knowledge sharing, training and standardization. Support business planning with data-driven insights. Overview The Infosys Financial Services unit is a global leader in driving digital transformation for financial institutions. We specialize in leveraging advanced technologies such as AI, cloud, and data-led innovation to help our clients accelerate growth and unlock business value. Our AI-driven solutions empower financial institutions to make smarter decisions, enhance customer experiences, and achieve operational excellence. Join us to be part of a pioneering team that is at the forefront of financial services transformation. You'll have the opportunity to work with cutting-edge AI technologies, collaborate with industry experts, and contribute to transformative projects across banking, wealth management, and insurance sectors. If you're passionate about AI and eager to make a significant impact, the Infosys Financial Services unit is the perfect place for you to grow and excel.

Requirements

Experience in Python, SQL, ML & statistical modeling.
Experience in Generative AI (LLMs, prompt engineering, RAG), Predictive Modeling.
Bachelor’s degree or foreign equivalent required from an accredited institution. Will also consider three years of progressive experience in the specialty in lieu of every year of education.
Candidates authorized to work for any employer in the United States without employer-based visa sponsorship are welcome to apply.

Nice To Haves

Experience with Mortgage domain.

Responsibilities

Develop data preparation tasks, while identifying patterns or anomalies.
Ensure data readiness for advanced modeling.
Develop models for complex use cases (e.g., forecasting models, LLM-based solutions), while refining algorithms to meet business needs, and ensure smooth deployment into scalable, production-ready solutions.
Conduct testing and optimize algorithms for performance, reliability, and scalability, while providing guidance to team members in best practices.
Design and develop predictive models and data-driven analyses to address business challenges.
Build, evaluate, and deploy models, standardize code, and contribute to knowledge management.
Leverage tools like SAS and R/Python to create reusable customizations for non-ML, ML, and deep learning algorithms, while enhancing analytics including LLMs, and create innovative, cost-effective solutions.
Define analytics problems for projects; execute visualization, analysis, and predictive modeling under guidance.
Proactively maintain models and implement improvements for accuracy and reliability.
Apply governance controls to mitigate risks and ensure compliance.
Analyze performance trends, recommend improvements, and document discrepancies for escalation.
Maintain comprehensive documentation standards, while participating in knowledge transfer sessions.
Participate in discussions with stakeholders to refine requirements, provide insights, and guide implementation of models.
Apply the predefined quality measurement framework at an individual task level in the project.
Deploy complex analytics tools or multi-system integration, while validating deployment success.
Participate in developing scripts or templates for repeated deployments tasks.
Contribute to analytic solutions, IP asset creation, and training initiatives.
Contribute to thought leadership such as papers, innovative non-ML, ML, deep learning or LLM models, and proofs of concepts.
Participate in and deliver analytics training, while contributing to content creation.
Provide input for segment and unit-level business plans.
Deliver scalable, high-quality analytics solutions aligned to business needs.
Drive innovation through advanced analytics, automation and thought leadership.
Enable team growth through knowledge sharing, training and standardization.
Support business planning with data-driven insights.