Senior Data Scientist

This is a fully remote position, open to applicants in United States.

📋 Description

• Oversee complete data science projects for predictive analytics applications, including demand forecasting, churn prediction, and risk assessment.

• Convert business needs into machine learning problem statements and determine suitable modeling strategies.

• Design, construct, and implement machine learning models utilizing traditional ML methods such as regression, classification, clustering, and time series analysis.

• Lead feature engineering, data preparation, and exploratory data analysis to enhance model efficacy.

• Create and manage scalable machine learning pipelines from data ingestion through to model deployment.

• Implement and oversee models on AWS utilizing services like SageMaker.

• Maintain model performance through validation, monitoring, and regular retraining.

• Collaborate with data engineering and MLOps teams to operationalize machine learning solutions.

• Employ best practices for model governance, interpretability, and ethical AI.

• Guide junior data scientists and deliver technical mentorship while remaining actively engaged.

• Effectively communicate insights, model results, and recommendations to business stakeholders.

⛳️ Requirements

• A minimum of 8 years of relevant hands-on technical experience in developing and implementing cloud solutions on AWS.

• Extensive experience leading predictive analytics projects utilizing traditional machine learning techniques, including regression, classification, clustering, and time series forecasting.

• Practical experience with time series forecasting models such as SARIMA, Prophet, and other ML-based forecasting methods.

• Proficient in Python, with experience in libraries such as scikit-learn, XGBoost, Pandas, and NumPy.

• Understanding of various machine learning techniques (supervised/unsupervised, etc.) like clustering, decision tree learning, and artificial neural networks, along with their practical pros and cons.

• Demonstrated ability to convert intricate business challenges into scalable machine learning solutions, driving feature engineering tactics and comprehensive model development.

• Practical experience with AWS Machine Learning services.

• Proven track record using AWS SageMaker, leveraging diverse data sources, training jobs, real-time and batch inference, and processing jobs.

• Experience leading model deployment on AWS SageMaker with a strong emphasis on performance enhancement, model governance, and quantifiable business impact.

• Implement and oversee MLOps-based model lifecycle management and best practices for ML architecture in live environments.

• Familiarity with at least one workflow orchestration tool, such as Airflow, Step Functions, SageMaker Pipelines, or Kubeflow.

• Capability to design end-to-end solution architecture for model training, deployment, and retraining utilizing native AWS services like SageMaker and Lambda functions.

• Experience in developing model monitoring and explainability workflows in production settings.

• Expertise in defining and implementing model governance frameworks and performance monitoring strategies in operational environments.

• Ability to work collaboratively with cross-functional teams, including developers, QA, project managers, and other stakeholders to understand their needs and deliver solutions.

• Experience in Generative AI development.

• Background in Infrastructure as Code (IaC) and CI/CD pipelines.

🏝️ Benefits

• Become part of one of the world's fastest-growing AI-first digital engineering firms and make a significant impact at scale.

• Lead and collaborate with a dynamic team of talented, motivated individuals tackling complex, meaningful challenges.

• Work with Fortune 500 companies and innovative disruptors in a research-oriented environment with over 60 patents.

• Stay at the forefront by gaining hands-on experience with advanced AI, ML, data, and cloud technologies while continuously enhancing your skills.