Senior Data Scientist

While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.


If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!

About Quantiphi:

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:

• 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