Senior Data Scientist

Quantiphi

Senior Data Scientist driving predictive analytics and machine learning initiatives for enterprise use cases. Building scalable, production-ready ML solutions using traditional techniques across various domains.

Posted 4/15/2026full-timeRemote • 🇺🇸 United StatesSeniorWebsite

Tech Stack

Tools & technologies

AirflowAWSCloudNumpyPandasPythonScikit-Learn

About the role

Key responsibilities & impact

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

Requirements

What you’ll need

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
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

Benefits

Comp & perks

Work where innovation happens
Upskill and discover your potential
Collaborate with a diverse and talented set of professionals

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learningpredictive analyticsregressionclassificationclusteringtime series forecastingfeature engineeringdata preparationmodel validationmodel retraining

Soft Skills

technical leadershipmentoringcommunicationcollaborationproblem-solving