Associate - Financial Services (Tech)

What you’ll do

1. Support the BCN case leads in problem solving, hypothesis generation, research and insight generation and quality control
2. Interpret, understand, and break down case requirements into actionable task items for the team-let
3. Lead meaningful and focused meetings on owned project workstreams, deliver business insights effectively to the project leadership
4. Be willing to take on challenging analytical problems and read research papers to come up with cutting-edge solutions from across disciplines to arrive at analytical solutions
5. Be part of the thought leadership exercise to establish a blue-print for cloud-based deployment of any analytical and/or data-driven solution
6. Ensure application performance and scale, maintaining high standards of code quality and thoughtful design
7. Support the process of Translating business problems into ML problems and create ML solutions to produce desired customer business outcomes.
8. Execute and deliver high quality outputs timely to internal and external stakeholders
9. Ability to derive actionable insights from voluminous quantitative and qualitative data/research
10. Provide day-to-day coaching to analysts on various aspects of projects, while also overseeing a workstream for 1-2 analysts
11. Gain proficiency in the key industry data sources KPIs along with developing expertise in analytical tools, models and languages
12. Gain proficiency in select consulting solutions (go-to-market strategy, revenue management, account management, brand strategy, etc.) delivered to and relevant for FS industry clients, along with expertise in one or more key sub-sectors within CP, covering consumer preferences, market trends, competitor landscape, etc.
13. Work on building and deploying advanced analytics solutions, including large-scale data processing, computationally intensive Optimization, Machine Learning and Deep Learning models
14. The focus will be on making the deployment sustainable and scalable, as per solution needs
15. Work on parallel processing of ML solutions, in an optimal manner
16. Collaborating with team members to make the solutions as client-ready as possible
17. (Good-to-have) Ability to deploy advanced analytics solutions on AWS/Azure

About You

1. Candidates should be graduates / post-graduates with a strong academic record
2. Work experience range in case the highest qualification is undergraduate studies – 2-5 years of exposure to management consulting, preferably in a global MNC environment
3. Work experience range in case highest qualification is postgraduate studies – 1-2 years of exposure to management consulting, preferably in a global MNC environment
4. Demonstrated technical experience working with technologies related to industrialization of artificial intelligence, machine learning and/or deep learning.
5. A mathematics and statistics background is preferred in addition to experience building complex machine learning models
6. Hands-on experience in building complex statistical models
7. Strong communications skills, should be able to drive discussion/presentations with senior stakeholders and client maps
8. Excellent implementation skills in Python and/or R, SQL and (preferably) PySpark with understanding of in analytical tools and techniques including / similar to Alteryx, Tableau, Power BI
9. Well versed in dealing with large datasets and using ML/DL libraries or packages
10. Deep understanding of the Gen-AI tooling ecosystem including knowledge and experience of Kubernetes, MLOps and AIOps tools (mandatory)
11. Proficient with Excel, and PowerPoint
12. (Good-to-have) Familiar with Cloud services (AWS) related to AI/ML
13. Architect and implement AI-driven solutions
14. Develop and deploy Generative AI-based solutions, leveraging cloud computing platforms (AWS/ Azure/GCP)
15. Hands-on experience with Python programming, microservices architecture, and cloud technologies (AWS)
16. Proven experience with database systems such as SQL Server, PostgreSQL, good to have knowledge of Snowflake, Oracle
17. Frontend and Backend Technologies: Exposure to HTML, CSS, Javascript, ReactJS and FastAPI, Django
18. Exposure to data analysis and visualization libraries like Matplotlib, Plotly, Pandas, Numpy etc.
19. Strong understanding of LLMs, NLP, Chains, Agents and AI model fine-tuning
20. Work in client-facing roles, understand business needs, and translate them into AI-driven solutions
21. Identify technical limitations, propose alternative approaches, and ensure the scalability of AI applications