Quantiphi is an award-winning, AI-First global digital engineering company that helps the world’s leading Fortune 1000 organizations transform bold ideas into measurable business impact. We go beyond building innovative AI technologies—we solve the problems that matter most to our clients.
Since our founding in 2013, Quantiphi has built a proven track record of turning complex challenges into meaningful outcomes across industries.
Headquartered in Boston, with more than 4,000 professionals worldwide, we partner with global enterprises to deliver large-scale digital, cloud, and AI-driven transformation. #SolvingWhatMatters.
We are an Elite and Premier partner to Google Cloud, AWS, NVIDIA, Snowflake, and other leading technology platforms, and our work has been recognized across the industry, including:
21 Google Cloud Partner of the Year awards in the past 10 years
3 AWS AI/ML Partner of the Year awards
3 NVIDIA Partner of the Year awards
3 Snowflake Partner of the Year awards
Rated Leaders by Gartner, Forrester, IDC, ISG, Everest Group and other leading analyst firms
Quantiphi delivers First-in-class AI solutions across Life Sciences, Healthcare, Banking, Financial Services, CPG, Manufacturing, Energy, High-Tech, Telecommunications, etc., powered by cutting-edge Generative AI and Agentic AI accelerators.
We are also proud to be certified as a Great Place to Work—reflecting our commitment to our people and our culture.
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:
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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.
What you will do:
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.
Basic Qualifications (BQ):
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.
Other Qualifications (OQ):
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:
Join one of the world’s fastest-growing AI-first digital engineering companies and make a real impact at scale.
Lead and collaborate with a high-energy team of talented, driven individuals solving complex, meaningful challenges.
Work with Fortune 500 companies and disruptive innovators in a research-driven environment with 60+ patents.
Stay ahead of the curve by gaining hands-on experience with cutting-edge AI, ML, data, and cloud technologies while continuously upskilling.
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us !
About Quantiphi
IT Services & Consulting4303 employeesFounded 2013
AI-first digital engineering company helping enterprises solve complex business problems with artificial intelligence, cloud, and data engineering.
