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, IT Services and IT Consulting.

Applicants for this role may also receive access to additional matched opportunities through the Careertakes platform.

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What You’ll Do

As an Associate Data Scientist (early-career), you will apply statistics, machine learning, and data science techniques to help solve business problems and contribute to Generative AI (GenAI) and agentic AI workflows. You will work with structured and unstructured data to deliver insights, evaluate model and workflow performance, and produce clear recommendations for stakeholders.

• Prepare, cleanse, join, and analyze structured, semi-structured, and unstructured data for analytics, machine learning, GenAI, and agentic AI workflows
• Implement and validate predictive, prescriptive, statistical, and machine learning models focused on measurable client and business outcomes
• Design experiments, features, prompts, evaluation datasets, metrics, and tests to assess model outputs, workflow accuracy, reliability, bias, and business value
• Build and evaluate GenAI, RAG (retrieval-augmented generation), and agentic AI workflows—assess retrieval quality, grounding, generated responses, tool usage, and end-to-end agent performance
• Collaborate in Agile teams with data scientists, data engineers, AI engineers, consultants, architects, and DBAs to apply analytical rigor and responsible AI practices
• Translate complex model results into clear, actionable recommendations for both technical and non-technical audiences
• Communicate with internal and external clients to understand business needs and select appropriate modeling approaches

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Qualifications (Required)

• Bachelor’s degree (or equivalent) in a quantitative field (Computer Science, Data Science, Statistics, Mathematics, Engineering, MIS, or similar)
• Strong fundamentals in mathematics, statistics, algorithms, and analytical reasoning
• Familiarity with programming in Python, SQL, R, or similar languages
• Foundational understanding of predictive/prescriptive modeling, statistical methods, machine learning, GenAI and agentic AI concepts
• Basic familiarity with cloud environments or AI platforms (AWS, Azure, Google Cloud, or similar)
• Strong problem solving, debugging, teamwork, and communication skills
• Willingness to travel up to 100%, based on project requirements

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Preferred Qualifications

• Course projects, internships, or portfolio work in data science, machine learning, analytics, GenAI, or agentic AI
• Familiarity with LLMs, embeddings, vector databases, retrieval systems, prompt workflows, and RAG
• Exposure to orchestration/agentic frameworks (LangChain, LlamaIndex, Semantic Kernel, AutoGen, etc.)
• Experience or awareness of Git, containers, APIs, testing frameworks, CI/CD, MLOps/LLMOps, model monitoring, data governance, privacy, security, and responsible AI practices

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Compensation & Benefits

• Base salary range: $88,800 – $133,200 per year (Confidential Client). This range is provided to comply with applicable pay-transparency rules.
• Early-career program benefits: access to structured learning (minimum 40 hours/year), industry-recognized certifications and digital badges, mentorship and professional communities
• Opportunities for internal mobility to explore different technologies, industries, and career paths
• Work in collaborative, cross-functional teams solving real client challenges with AI and data analytics

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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.