Elevate Your Impact Through Innovation and Learning:

Evalueserve is a global leader in delivering innovative and sustainable solutions to a diverse range of clients, including over 30% of Fortune 500 companies. With a presence in more than 45 countries across five continents, we excel in leveraging state-of-the-art technology, artificial intelligence, and unparalleled subject matter expertise to elevate our clients' business impact and strategic decision-making. Our team of over 4,500 talented professionals operates in countries such as India, China, Chile, Romania, the US, and Canada. Our global network also extends to emerging markets like Colombia, the Middle East, and the rest of Asia-Pacific. Recognized by Great Place to Work® in India, Chile, Romania, the US, and the UK in 2022, we offer a dynamic, growth-oriented, and meritocracy-based culture that prioritizes continuous learning and skill development, work-life balance, and equal opportunity for all.

About Data Analytics (DA):

Data Analytics is one of the highest growth practices within Evalueserve, providing you rewarding career opportunities. Established in 2014, the global DA team extends beyond 1000+ (and growing) data science professionals across data engineering, business intelligence, digital marketing, advanced analytics, technology, and product engineering. Our more tenured teammates, some of whom have been with Evalueserve since it started more than 20 years ago, have enjoyed leadership opportunities in different regions of the world across our seven business lines.

What you will be doing at Evalueserve:

Model Development: Design, develop, deploy and monitor Machine Learning Models:

• Machine Learning techniques like Linear Regression, Logistics Regression, Bagging and Boosting algorithms (Random Forest, XGBoost, GBM, etc.), LSTM, K Nearest Neighbor, Neural Networks, Time series Forecasting, and other advanced algorithms.
• MLOps – Development, deploying and Monitoring model performance using pipelines – Docker and Kubernetes.
• Advanced AI/ML- Computer Vision/OCR, NLP and text analytics.
• AI/ML use case development for Core banking domain, retail banking customers product and services such as profiling, clustering, segmentation, propensity modelling, churn, next best offer/next best product, cross sell and upsell etc.
• Visualization – Integrating Model outputs via Dashboard such as Tableau and PowerBI.
• Analyzing, pre-processing, Exploratory Data Analysis, Feature Engineering from wide range of data sources such as Enterprise Datawarehouse, Data Lake.
• SQL scripting on databases like Teradata, Oracle, etc.

Collaboration: Work closely with product managers, data scientists, and software engineers to define project requirements and deliver effective solutions.

Data Handling: Gather, process, analyze large datasets training for generative models.

Research: Stay updated with the latest advancements in generative AI and related fields to enhance methodologies and technologies.

Performance Tuning: Optimize models for performance and scalability, ensuring they meet production standards.

Documentation: Maintain comprehensive documentation of processes, models, and experiments to facilitate knowledge sharing within the team.

Mentorship: Provide guidance to junior team members, fostering a collaborative and learning-oriented environment.

What we're looking for:

• We are looking for 8-12 years experienced Data Science/Machine Learning Engineer to join our innovative team. In this role, you will develop, implement and monitor/re-train Machine Learning models to be deployed on-premises locally on client network and/or Cloud platform, to enhance our products and services. Your expertise will help us leverage cutting-edge technologies to drive business success and improve user experiences.

Technical Requirements:

Programming Languages: Proficiency in Python for model development and data manipulation.

Frameworks and Libraries: NumPy, Pandas, Scikit-learn, Matplotlib, XGBoost, LightGBM, CatBoost, PyCaret, Keras ,TensorFlow, PyTorch, OpenCV, spaCy, Statsmodels

Cloud Computing: Experience with AWS, Azure, or Google Cloud Platform for model training and deployment. Familiarity with Docker and Kubernetes for managing containerized applications.

Version Control: Proficient in using Git for version control and collaboration in software development.

MLOps: Experience with development, deployment and monitoring pipelines using Docker and Kubernetes.

Database : Experience with Data Exploration using SQL including PL/SQL Scripting and Understanding of Datwarehouse concepts.

Visualization: Integration of ML model output using BI tools such as PowerbI and Tableau.

Other Skills: NLP, Computer Vision and Reinforcement Learning. Strong problem-solving and analytical skills with excellent communication and presentation skills.

Domain Requirements:

• It would be preferable to have significant exposure to banking domain via past working experiences with Bank and Financial organizations.
• Expertise in the areas of Core Banking, Retail banking and services such as Deposits, Loans (Home Loans, Mortgages, Personal Loans), Credit Cards, Overdrafts etc.
• Mandatory hands-on experience with Model Development for use cases such as Hyper personalization – Next Best Offer/Next Best Action, customer profiling and segmentation, campaign offer generation etc., for Retail banking customers.

What you’ll need to have:

• Bachelors/Master’s degree in Computer science/Data Science/ AI-ML.
• 8-12 years of hands-on experience in Data Science /Machine Learning /Deep Leaning Model development and deployment.
• 3-5 years of proven experience in Core Banking Analytics use cases.
• Extensive experience in handling big datasets and working on Python, Teradata, Oracle, MLOps-Deployment, Deployment and Monitoring, SQL, Tableau, PowerBI.
• Extensive experience in Banking and Finance Fundamentals.
• They should also have excellent problem-solving and communication skills, as well as a commitment to delivering high-quality results.

Disclaimer:

• The following job description serves as an informative reference for the tasks you may be required to perform. However, it does not constitute an integral component of your employment agreement and is subject to periodic modifications to align with evolving circumstances.