Senior Data Scientist, SageMaker, Bedrock

Job not on LinkedIn

🔥 0 minutes ago

🇺🇦 Ukraine – Remote

⏰ Full Time

🟠 Senior

📊 Data Scientist

👻 Ghost score 12%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Automat-it

WebsiteLinkedInAll Job Openings

51 - 200 employees

💼 Consulting

📦 Logistics

📣 Marketing

Consulting • Logistics • Marketing

Automat-it is an all-in AWS Premier partner empowering startups with DevOps & FinOps expertise and hands-on services. Founded in 2012 by CEO Ziv Kashtan, Automat-it has guided and supported hundreds of startups to leverage AWS smarter throughout their growth journey. Specializing in DevOps, Cloud services, DBA, IT infrastructure, and Performance, they build cloud solutions from the DevOps perspective to optimize cloud performance and economics for their customers.

📋 Description

• Own Data Science projects end-to-end, from technical discovery through data analysis, solution design, experimentation, implementation, deployment, and production validation • Evaluate and select technical approaches including prompt engineering, RAG, GenAI, agentic workflows, fine-tuning, smaller language models, classical ML, Computer Vision, recommendation systems, and custom model training • Analyze and prepare customer datasets, identify data quality issues, create representative validation and golden datasets, and assess data suitability for modeling • Train, fine-tune, optimize, evaluate, and deploy ML models using Python, PyTorch/TensorFlow, SageMaker, and modern ML tooling • Build and evaluate Generative AI solutions using Amazon Bedrock, RAG, prompt engineering, model selection, agentic workflows, and AWS-native AI services • Use SageMaker Studio, training jobs, endpoints, pipelines, model registry, batch inference, monitoring, and other production ML capabilities • Design production-ready architectures balancing quality, latency, cost, scalability, maintainability, observability, and operational complexity • Participate in customer technical discovery, workshops, architecture discussions, and delivery conversations with founders, CTOs, engineering teams, and technical stakeholders • Challenge technical assumptions, explain trade-offs and costs, and recommend simpler or more effective approaches • Independently own projects with minimal supervision and mentor less experienced Data Scientists and engineers when needed • Collaborate with AI Engineers, MLOps, Data Engineering, DevOps, and Solution Architecture teams on cross-domain customer solutions

🎯 Requirements

• 5+ years of experience in Data Science, Machine Learning, Applied Science, or a closely related role • Strong classical Machine Learning fundamentals and hands-on experience building production ML solutions • Practical experience with data preparation, validation, feature engineering, dataset construction, and model evaluation • Strong Python skills and hands-on experience with PyTorch and/or TensorFlow • Strong practical experience with AWS SageMaker beyond notebook-level usage, including model training, deployment, inference, pipelines, or production operations • Hands-on experience with Amazon Bedrock and modern Generative AI approaches • Practical experience with model fine-tuning and understanding when fine-tuning is preferable to prompting, RAG, or other approaches • Experience in at least one ML domain such as NLP, Computer Vision, recommendation systems, forecasting, structured ML, or multimodal ML • Understanding of RAG, embeddings, prompt engineering, foundation models, and agentic workflows • Strong understanding of MLOps and production ML practices, including model deployment, monitoring, reproducibility, lifecycle management, and CI/CD • Experience designing and owning solutions independently rather than working only from predefined technical specifications • Strong customer-facing communication skills and ability to explain technical trade-offs clearly • Ability to work with ambiguity, messy real-world data, and changing customer requirements • Strong technical judgment and a pragmatic approach to balancing model quality with delivery speed, cost, and business value • Experience with AgentCore, Bedrock Agents, LangGraph, Strands Agents, or other agentic frameworks is a plus • Experience with Small Language Models or domain-specific model adaptation is a plus • Experience with recommendation systems, audio ML, signal processing, or multimodal systems is a plus • Experience in technical consulting, pre-sales, or customer discovery is a plus • AWS Machine Learning certifications are a plus • Master’s degree or PhD in Computer Science, Machine Learning, Data Science, Mathematics, Statistics, or a related field is a plus • CV must be submitted in English

🏖️ Benefits

• Sponsored AWS certifications • Access to internal “Expert-led” knowledge sharing • Early access to beta AWS features • Clear progression and defined career paths from engineering to leadership • Collaborative, supportive, and transparent culture • Mentorship and collective success • Opportunity to work with cutting-edge AWS, AI, DevOps, FinOps, and Kubernetes technologies • Opportunity to work on large-scale, meaningful projects with startups and scaleups • Global reach with stability of a mature, profitable company and the agility of a startup • Equal-opportunity workplace