Data Scientist
The Data Scientist at Confidential Client leads applied AI and machine learning projects, developing and deploying models for security and developer-focused use cases such as anomaly and malicious behavior detection. Acting as an internal AI consultant, this role collaborates across teams to translate research into scalable, practical AI solutions while ensuring compliance with data privacy and ethical standards. The position requires strong expertise in modern AI technologies, LLM ecosystems, and hands-on experience building production-ready ML and generative AI applications.
About Careertakes
👉 Important disclosure: Careertakes is a third-party recruiting platform supporting this hiring process. If selected, you will be employed directly by our client, Software Development (software supply chain security / DevSecOps / SaaS).
Applicants for this role may also receive access to additional matched opportunities through the Careertakes platform.
Position overview
Confidential Client is seeking an experienced Data Scientist to join a growing AI & Data Science organization. You will act as an internal AI consultant and technical lead, partnering with product, engineering, and security teams to design, prototype, and deploy machine-learning and generative-AI solutions — from anomaly and malicious-behavior detection to developer- and analyst-facing GenAI experiences. This is a full-time, remote-friendly role based in Atlanta, GA (TELECOMMUTE).
What You’ll Do
- Lead applied AI projects end-to-end: prototype, validate, and help deploy ML and GenAI solutions that deliver measurable business impact.
- Serve as an internal consultant: scope problems, recommend approaches, and advise teams on ML/AI best practices and productive use of generative tech.
- Research, develop, and deploy models for use cases such as malicious behavior detection, anomaly detection, and fraud analysis using classical ML, embeddings, LLMs, retrieval-augmented generation (RAG), and agentic workflows.
- Design robust experiments and evaluation pipelines: cross-validation, drift monitoring, ground-truthing, and business-impact metrics.
- Translate research into production: build scalable APIs, tools, or workflows that enable other teams to adopt AI safely and effectively.
- Explore and evaluate new LLM and GenAI techniques to enhance developer and security workflows.
- Communicate technical tradeoffs and recommendations clearly to technical and non-technical stakeholders; mentor peers and elevate organizational AI literacy.
- Partner with data governance to ensure privacy, compliance, and ethical use of customer data.
What You Bring
- 5+ years of hands-on experience in applied data science, machine learning, AI engineering, or AI research.
- Strong software/data background (Computer Science or equivalent technical degree preferred).
- Proficient in Python and experience with data/AI libraries and platforms (e.g., scikit-learn, Databricks, LLM APIs).
- Track record of building and shipping ML or GenAI applications from prototype to usable internal or customer-facing workflows.
- Deep familiarity with modern LLM ecosystems (OpenAI, Anthropic/Claude, Hugging Face, and open-weight models).
- Experience designing LLM applications using prompting, context management, structured outputs, retrieval, and tool use.
- Practical experience building multi-step or agentic AI workflows (LangChain, LangGraph, Semantic Kernel, or similar).
- Strong evaluation mindset — define quality metrics, build representative evaluation sets, and assess reliability.
- Comfortable working with large, messy structured and unstructured data to extract features, insights, and visualizations.
- Proficiency with Git, testing, code review, and collaborative software-development practices.
- Clear, proactive communication and balanced technical judgment when integrating emerging AI capabilities into maintainable systems.
Nice to Have
- MLOps experience (MLflow or similar), experiment tracking, reproducible pipelines, CI/CD, serving, and production monitoring.
- Experience operating ML/GenAI systems at scale: observability, tracing, incident response, and drift detection.
- Familiarity with Databricks ML, AWS SageMaker, Azure ML, or similar managed ML platforms.
- Knowledge of LLM guardrails, production safety practices, and agent-tool integrations.
- Experience with AI-assisted development tools (Copilot, Claude Code, Codex).
- Background in cybersecurity, fraud detection, anomaly detection, code analysis, or software supply-chain security.
- Experience with PySpark and production data pipelines.
- Prior experience at a software product company or SaaS environment.
Why You'll Like Working Here
- Join a team applying cutting-edge ML and GenAI to real security and developer-experience problems.
- Work with mature data engineering and product teams so you can focus on modeling and delivery.
- Remote-friendly setup with opportunities to work across multiple product and research domains.
- Collaborative culture that values autonomy, mentorship, and responsible AI practices.
Equal Opportunity & Hiring Transparency
Careertakes and our client are Equal Opportunity Employers committed to building a diverse and inclusive workforce. We prohibit discrimination or harassment of any kind. To support a fair and efficient hiring process, AI tools may be used to assist with application review or resume screening. These tools do not replace human decision-making. Final hiring decisions are made by people.
If you have questions about how your data is used, please contact us directly.
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