Senior Data Scientist - AWS

About Quantiphi:

• Quantiphi is an award-winning, AI-First digital engineering and consulting company focused on delivering high-impact Services and Solutions that help organizations solve what truly matters. We partner with enterprises to reimagine their businesses through intelligent, scalable, and transformative AI—driving measurable outcomes at the very core of their operations.
• Since our founding in 2013, Quantiphi has tackled some of the world’s most complex business challenges by combining deep industry expertise, disciplined cloud and data engineering practices, and cutting-edge applied AI research. Our work is rooted in delivering accelerated, quantifiable business value—not just technology for technology’s sake.
• Headquartered in Boston, Quantiphi is a global organization with 4,000+ professionals serving clients across key industry verticals, including BFSI, Healthcare & Life Sciences, CPG, MFG, TME etc. As an Elite and Premier partner to leading cloud and AI platforms such as NVIDIA, Google Cloud, AWS, and Snowflake, we build and deliver enterprise-grade AI services and solutions that create real-world impact.

We have been recognized with:

• 3x AWS AI/ML award wins.
• 3x NVIDIA Partner of the Year titles.
• Recognized Leaders by Gartner, Forrester, IDC, ISG, Everest Group and other leading analyst and independent research firms.
• We offer first-in-class industry solutions across Healthcare, Financial Services, Consumer Goods, Manufacturing, and more, powered by cutting-edge Generative AI and Agentic AI accelerators.
• We have been certified as a Great Place to Work for the third year in a row- 2021, 2022, 2023.

Be part of a trailblazing team that’s shaping the future of AI, ML, and cloud innovation. Your next big opportunity starts here!

For more details, visit: Website or LinkedIn Page.

Role: Senior Data Scientist (AWS)

Experience Level: 8+ years

Employment type: Full Time

Location: Remote (USA)

Description:

• We are looking for a Senior Data Scientist to drive predictive analytics and machine learning initiatives for enterprise use cases. This role focuses on building scalable, production-ready ML solutions using traditional machine learning techniques across domains such as forecasting, risk modeling, and customer analytics.
• The ideal candidate will be a hands-on leader who can translate business problems into data-driven solutions, define modeling strategies, and lead end-to-end implementation on AWS while ensuring measurable business impact.

Key Responsibilities:

• Lead end-to-end data science initiatives for predictive analytics use cases such as demand forecasting, churn prediction, and risk modeling.
• Translate business requirements into ML problem statements and define appropriate modeling approaches.
• Design, build, and deploy machine learning models using traditional ML techniques (regression, classification, clustering, time series).
• Drive feature engineering, data preparation, and exploratory data analysis to improve model performance.
• Develop and manage scalable ML pipelines from data ingestion to model deployment.
• Deploy and manage models on AWS using services such as SageMaker.
• Ensure model performance through validation, monitoring, and periodic retraining.
• Collaborate with data engineering and MLOps teams to productionize ML solutions.
• Apply best practices for model governance, explainability, and responsible AI.
• Mentor junior data scientists and provide technical leadership while remaining hands-on.
• Communicate insights, model outputs, and recommendations effectively to business stakeholders.

Skills:

• 8+ years of relevant hands-on technical experience implementing, and developing cloud solutions on AWS.
• Strong experience leading predictive analytics initiatives using traditional ML techniques including regression, classification, clustering, and time series forecasting.
• Hands-on experience with time series forecasting models including SARIMA, Prophet, and other ML-based forecasting approaches.
• Proficiency in Python with experience in libraries such as scikit-learn, XGBoost, Pandas, NumPy.
• Knowledge of a variety of machine learning techniques (Supervised/unsupervised etc.) (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
• Proven ability to translate complex business problems into scalable ML solutions, driving feature engineering strategies and end-to-end model development.
• Hands-on experience on AWS Machine Learning services. Proven experience using AWS Sagemaker leveraging different types of data sources, Training jobs, real-time and batch Inference, and Processing Jobs.
• Experience leading model deployment on AWS SageMaker with a strong focus on performance optimization, model governance, and measurable business impact.
• Implement and manage MLOps based model lifecycle and best practices for ML architecture in production environments.
• Experience with at least one of the workflow orchestration tools, Airflow, StepFunctions, SageMaker Pipelines, Kubeflow etc.
• Ability to create end to end solution architecture for model training, deployment and retraining using native AWS services such as Sagemaker, Lambda functions, etc.
• Experience in building model monitoring and explainability workflows in production environments.

Nice to have:

• Experience defining and driving model governance frameworks and performance monitoring strategies in production environments.
• Ability to collaborate with cross-functional teams such as Developers, QA, Project Managers, and other stakeholders to understand their requirements and implement solutions.
• Experience with Generative AI development.
• Experience working on Infrastructure as Code (IaC) and CI/CD pipelines

What is in it for you:

• Make an impact at one of the world’s fastest-growing AI-first digital engineering companies.
• Upskill and discover your potential as you solve complex challenges in cutting-edge areas of technology alongside passionate, talented colleagues.
• Work where innovation happens - work with disruptive innovators in a research-focused organization with 60+ patents filed across various disciplines.
• Stay ahead of the curve—immerse yourself in breakthrough AI, ML, data, and cloud technologies and gain exposure working with Fortune 500 companies.