About the Job
About Aligned Automation
At Aligned Automation, we live by our "Better Together" philosophy to build a better world. As a strategic service provider to Fortune 500 companies, we help digitize enterprise operations and drive impactful business strategies. Our purpose goes beyond projects—we strive to deliver meaningful, sustainable change that shapes a more optimistic and equitable future.
Our culture is deeply rooted in our 4Cs—Care, Courage, Curiosity, and Collaboration—ensuring that each employee is empowered to grow, innovate, and thrive in an inclusive workplace.
We're looking for a seasoned Data Scientist to lead the design and delivery of machine learning and AI systems that power our products. This is a hands-on leadership role for someone who has grown alongside the field itself from classical data science and predictive modeling, through the rise of generative AI, and into today's Agentic systems. You'll set technical direction, mentor a team of data scientists and ML engineers, and stay close enough to the work to build and ship production systems yourself.
You'll partner with product, engineering, and business stakeholders to identify high-impact opportunities, translate ambiguous problems into well-scoped ML solutions, and take them all the way from prototype to reliable production deployment.
What You'll Do
Lead the end-to-end lifecycle of data science and AI initiatives, from problem framing and data strategy through modeling, deployment, and monitoring. Build and mentor a high-performing team, providing technical guidance, career development, and code/design review. Architect and deliver GenAI and Agentic solutions including retrieval-augmented generation, fine-tuning, multi-agent workflows, and orchestration — that operate reliably at scale. Establish best practices around experimentation, model evaluation, MLOps, and responsible AI. Collaborate with cross-functional partners to align technical work with business outcomes and communicate results to both technical and non-technical audiences. Own the production health of deployed models, including performance, cost, latency, and drift.
Must-Have
- 8–12 years of hands-on experience in data science and machine learning, with a track record of shipping models to production.
- Demonstrated career progression across the field: classical/traditional data science (statistical modeling, forecasting, classification, recommendation, etc.), through generative AI (LLMs, RAG, fine-tuning, prompt engineering), and into Agentic AI (autonomous/multi-agent systems, tool use, orchestration frameworks).
- Proven experience leading and mentoring a team of data scientists or ML engineers, including work allocation, technical review, and people development.
- Strong programming skills in Python and its ML ecosystem (e.g., pandas, scikit-learn, PyTorch or TensorFlow).
- Deep understanding of the full ML lifecycle and MLOps: experimentation, CI/CD for models, deployment, monitoring, and retraining.
- Experience with GenAI/Agentic tooling such as LangChain, LlamaIndex, LangGraph, vector databases, and major LLM providers.
- Solid foundation in statistics, ML theory, and model evaluation.
- Experience deploying and operating solutions
- Excellent communication skills and the ability to influence technical and business stakeholders.
Good-to-Have
- Experience with responsible AI, model governance, and evaluation frameworks for LLM/Agentic systems.
- Contributions to open source, publications, or conference talk
- Advanced degree (MS/PhD) in Computer Science, Statistics, Mathematics, or a related quantitative field.
