Data Scientist
CyberCoders
Job Description
Job Description
Data Scientist
Position Overview
We are seeking a pragmatic, results-oriented Data Scientist focused on Applied AI & ML to design, develop, and deploy production-ready machine learning and LLM-powered solutions. The role combines strong ML engineering, LLM and RAG experience, data platform know-how (lakehouse, Azure, Fabric), and business intelligence skills to support analytic products, data-driven decisions, and agent/AI quality. You will partner with cross-functional teams to translate business problems into scalable AI solutions, implement retrieval-augmented generation (RAG) systems, and maintain data pipelines and monitoring for model performance and reliability.
- Design, develop, and deploy machine learning models and LLM-based solutions (including retrieval-augmented generation) to solve business problems and automate decision processes.
- Build and maintain data pipelines and lakehouse integrations on Azure and Microsoft Fabric to ensure reliable data access for training, inference, and BI reporting.
- Implement, evaluate, and optimize LLMs and prompt engineering techniques for production use, including fine-tuning, retrieval strategies, and hybrid retrieval-indexing architectures.
- Collaborate with engineering, product, and analytics teams to define requirements, deliver prototypes, production models, and end-to-end solutions that provide measurable business value.
- Develop tooling and processes for model governance, versioning, monitoring, A/B testing, and agent quality assessment to ensure robust, safe, and auditable AI systems.
- Create and maintain SQL queries, Python scripts, and ETL processes to support feature engineering, training data preparation, and operational inference workloads.
- Deliver BI dashboards and reports (Power BI) and provide data support to stakeholders, translating model outputs into actionable insights and recommendations.
- Optimize model performance and cost across cloud infrastructure, including compute, storage, and inference pipelines on Azure.
- Document solutions, best practices, and runbooks; mentor junior team members and contribute to continuous improvement of AI/ML practices.
Qualifications
- Bachelors or Masters degree in Computer Science, Data Science, Statistics, Engineering, or a related field; PhD preferred but not required.
- 3+ years experience in applied machine learning, data science, or ML engineering, with a track record of delivering production ML or AI solutions.
- Hands-on experience with large language models (LLMs), prompt engineering, fine-tuning, and building retrieval-augmented generation (RAG) systems.
- Strong programming skills in Python and experience writing performant SQL for feature extraction and analysis.
- Experience with cloud platforms preferably Azure and familiarity with Microsoft Fabric, lakehouse architectures, and cloud-native data services.
- Proficiency in BI tools, particularly Power BI, to build dashboards and communicate analytical insights to business stakeholders.
- Experience with model monitoring, A/B testing, model governance, and agent quality evaluation for conversational agents or automated decision systems.
- Solid understanding of machine learning fundamentals (supervised/unsupervised learning, evaluation metrics, feature engineering) and MLOps practices.
- Excellent communication skills and ability to work cross-functionally to translate business needs into technical solutions.
- Preferred: experience with additional tools and libraries for LLM deployment (e.g., LangChain, Hugging Face, Azure OpenAI), and familiarity with containerization and CI/CD for ML workflows.
Benefits
- Health
- Dental
- Vision
- 401(k)
- Profit Sharing
- On-Site Fitness Center
- PTO + Floating Holidays
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