Note: The job is a remote job and is open to candidates in USA. myTrudy is a company focused on enhancing the job search experience through AI and personality insights. They are seeking a Data Scientist & AI Engineer to design and deploy machine learning models, build causal inference frameworks, and create LLM-powered applications, collaborating with various stakeholders to turn data into impactful solutions.
Responsibilities
- Design, train, and deploy production machine learning models (classification, regression, clustering, NLP, time series) to solve business problems
- Apply causal inference techniques (e.g., difference-in-differences, instrumental variables, propensity score matching) to measure the true impact of product changes and business interventions
- Build and maintain robust data pipelines and feature engineering workflows that feed ML systems
- Conduct rigorous A/B testing and experimentation, translating statistical results into clear business recommendations
- Architect and build LLM-powered applications, agents, and workflows using LangChain, LangGraph, and related frameworks
- Design retrieval-augmented generation (RAG) systems, tool-calling agents, and multi-step reasoning pipelines
- Evaluate and benchmark LLM performance; implement guardrails, prompt engineering best practices, and evaluation frameworks
- Stay current with the rapidly evolving AI landscape and advise the team on which new tools and techniques to adopt
- Partner with product managers, engineers, and business leaders to identify high-impact opportunities for data science and AI
- Communicate complex technical work clearly to non-technical stakeholders through compelling visualizations and presentations
- Contribute to team knowledge-sharing through documentation and code reviews
Skills
- 3+ years of professional experience in data science, machine learning engineering, or a closely related role
- 2+ years of experience working with web platforms
- Strong proficiency in Python and its data science ecosystem (pandas, scikit-learn, NumPy, etc.)
- Hands-on experience building and deploying ML models in production environments
- Working knowledge of causal inference methods and experimental design (A/B testing, quasi-experimental methods)
- Demonstrated experience building applications with LangChain, LangGraph, or comparable LLM orchestration frameworks
- Familiarity with LLM concepts: prompt engineering, RAG, embeddings, vector databases, agent architectures
- Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field (or equivalent practical experience)
- Experience with deep learning frameworks (PyTorch, TensorFlow)
- Experience with Weaviate and pgvector
- Familiarity with cloud ML services (AWS SageMaker, GCP Vertex AI, Azure ML)
- Exposure to graph-based AI workflows, multi-agent systems, or autonomous AI pipelines
- Contributions to open-source projects or published research in ML/AI
Company Overview