Senior Data Scientist/ML Engineer to $7000

ABOUT CLIENT

An international organization with over 20 years of experience in advancing professional medical education and improving patient care. It focuses on evidence-based medicine and delivers educational programs in oncology, cardiology, and women’s health, reaching a global audience of healthcare professionals.
 

PROJECT TECH STACK

MS PowerBI, QLIK, MS Office (Excel, PowerPoint, Docs)
 

PROJECT STAGE

Live product
 

QUALIFICATIONS AND SKILLS

• Data Engineering Background: 5+ years of experience in data engineering, with a strong understanding of data pipelines, architectures, and tools (e.g. Apache Beam, Apache Spark, AWS Glue);
• Machine Learning Experience: 3+ years of experience in machine learning engineering and/or data science, with a focus on traditional AI techniques;
• Strong programming skills in Python, with experience working with machine learning libraries and frameworks;
• Experience working with graph databases and querying languages (e.g. Cypher, SPARQL);
• Familiarity with SageMaker and/or other cloud-based machine learning platforms;
• Strong understanding of machine learning algorithms and techniques, including supervised and unsupervised learning;
• Ability to make informed decisions about model selection, deployment, and maintenance;
• Experience working on large-scale products and data science projects.
   

NICE TO HAVE

• Experience with generative AI techniques, such as RAG optimization and agentic workflows;
• PhD in Computer Science, Statistics, or related field (not required, but a huge plus);
• Experience working with large-scale datasets and distributed computing environments;
• Background in Ad tech and marketing tech.
   

RESPONSIBILITIES

• Optimize machine learning models – primarily using traditional AI techniques (approx. 80%) with selective application of generative AI solutions (approx. 20%);
• Leverage AWS SageMaker and other AWS services to build, refine, and deploy ML models, improving existing implementations without starting from scratch;
• Analyze large, multi-platform marketing and advertising datasets, translating results into actionable insights for a unified MarTech/AdTech backend platform;
• Collaborate closely with data engineering, backend development, and product teams to integrate algorithms into production workflows, ensuring scalability and performance;
• Strategize and recommend optimal AI/ML approaches without bias toward a specific technology, balancing innovation with practical application;
• Support automation initiatives in marketing analytics, contributing to feature engineering, workflow optimization, and integration of agentic AI components