Data Science Strategist

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hyderabad, India beBeeMachinelearningleader Full time

Job OverviewWe are looking for a seasoned leader in data science to oversee the entire process of developing and implementing AI solutions on the AWS platform.The ideal candidate will have extensive experience in machine learning, experimentation, and advanced analytics, with a strong track record of delivering measurable business results.Key ResponsibilitiesProblem Definition & Stakeholder Collaboration: Collaborate with business, product, and engineering teams to translate ambiguous problems into clear data science use cases and success metrics.Data & Feature Engineering (AWS-native): Design and build robust data pipelines using AWS services such as S3, Glue, Athena, Redshift, EMR, Lambda, Step Functions.Modeling & Analytics: Build, train, and optimize models for use cases such as prediction, recommendation, forecasting, personalization, segmentation, anomaly detection, etc.MLOps & Deployment (AWS SageMaker): Productionize models using Amazon SageMaker (training, tuning, endpoints, pipelines, model registry).Leadership & Mentoring: Provide technical leadership on projects, setting standards for experimentation, documentation, and code quality.Requirements7+ years of hands-on experience in data science or applied machine learning roles.Strong proficiency in Python and ML/data libraries (pandas, numpy, scikit-learn, XGBoost, PyTorch/TensorFlow).Demonstrated experience building and deploying ML solutions on AWS.Solid understanding of statistics, experimental design, and causal inference.BenefitsThis role offers the opportunity to work on cutting-edge AI projects, collaborate with talented engineers and scientists, and drive business impact through data-driven decision making.Candidates with the following qualifications will be given preference:Experience with streaming data / real-time ML using Kinesis, Kafka/MSK, or similar.Experience with feature stores and ML observability tools.Domain experience in one or more areas such as marketing analytics, customer personalization, fraud/risk, pricing, demand forecasting, or operations optimization.

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