Applied AI ML Lead

Job Responsibilities

• Research, develop, and implement machine learning algorithms to solve complex problems related to personalized financial services in retail and digital banking domains.
• Work closely with cross-functional teams to translate business requirements into technical solutions and drive innovation in banking products and services.
• Collaborate with product managers, key business stakeholders, engineering, and platform partners to lead challenging projects that deliver cutting-edge machine learning-driven digital solutions.
• Conduct research to develop state-of-the-art machine learning algorithms and models tailored to financial applications in personalization and recommendation spaces.
• Design experiments, establish mathematical intuitions, implement algorithms, execute test cases, validate results, and productionize highly performant, scalable, trustworthy, and often explainable solutions.
• Collaborate with data engineers and product analysts to preprocess and analyze large datasets from multiple sources.
• Stay up-to-date with the latest publications in relevant Machine Learning domains and find applications for the same in your problem spaces for improved outcomes.
• Communicate findings and insights to stakeholders through presentations, reports, and visualizations.
• Work with regulatory and compliance teams to ensure that machine learning models adhere to standards and regulations.
• Mentor Junior Machine Learning associates in delivering successful projects and building successful careers in the firm.
• Participate and contribute back to firm-wide Machine Learning communities through patenting, publications, and speaking engagements.
  

Required Qualifications, Capabilities And Skills

• Expert in at least one of the following areas: Natural Language Processing, Knowledge Graph, Computer Vision, Speech Recognition, Reinforcement Learning, Ranking and Recommendation, or Time Series Analysis.
• Deep knowledge in Data structures, Algorithms, Machine Learning, Data Mining, Information Retrieval, Statistics.
• Demonstrated expertise in machine learning frameworks: Tensorflow, Pytorch, pyG, Keras, MXNet, Scikit-Learn.
• Strong programming knowledge of python, spark; Strong grasp on vector operations using numpy, scipy; Strong grasp on distributed computation using Multithreading, Multi GPUs, Dask, Ray, Polars etc.
• Strong analytical and critical thinking skills for problem solving.
• Excellent written and oral communication along with demonstrated teamwork skills.
• Demonstrated ability to clearly communicate complex technical concepts to both technical and non-technical audiences
• Experience in working in interdisciplinary teams and collaborating with other researchers, engineers, and stakeholders.
• A strong desire to stay updated with the latest advancements in the field and continuously improve one's skills
  

Preferred Qualification, Capabilities And Skills

• Deep hands-on experience with real-world ML projects, either through academic research, internships, or industry roles.
• Experience with distributed data/feature engineering using popular cloud services like AWS EMR
• Experience with large scale training, validation and testing experiments.
• Experience with cloud Machine Learning services in AWS like Sagemaker.
• Experience with Container technology like Docker, ECS etc.
• Experience with Kubernetes based platform for Training or Inferencing.
• Contributions to open-source ML projects can be a plus.
• Participation in ML competitions (e.g., Kaggle) and hackathons demonstrating practical skills and problem-solving abilities.
• Understanding of how ML can be applied to various domains like healthcare, finance, robotics, etc.
  

ABOUT US