Zenon
Website:
zenon.ai
Company:
https://www.linkedin.com/company/zenonai
Seniority: Mid-Senior level
Industries: IT Services and IT Consulting
Job details:
Company Description
Zenon is a boutique firm focused on financial services, healthcare, and private equity. We are deeply experienced in these industries, and provide analytics, data, and intelligent solutions to our clients.
Our team includes some of the world’s most accomplished data scientists along with solutions architects, industry experts, and execution teams who know how to bring AI and machine learning into front line decision-making and ongoing operations.
About the Role
Zenon Analytics is looking for an Analytics Lead / Manager (5–8 years of experience) to own end-to-end analytics and AI/ML engagements for our clients. This is a hands-on leadership role: you will structure ambiguous business problems, build and productionize ML/AI solutions, and guide a small team of analysts through delivery — while staying close enough to the data and code to unblock them when needed.
We’re looking for a mix of skills that leans more toward Analytics and Machine Learning, with working exposure to Data Engineering and enough Software Engineering (full-stack, Python) to be a credible partner to engineering teams during deployment. You don’t need to be deep in all four — but you should be genuinely comfortable moving between them.
What You Will Do
• Lead the design and development of statistical, ML, and AI-driven solutions (including LLM-based and agentic workflows) for a range of business problems.
• Guide the team on exploratory data analysis, feature engineering, model development, validation, and deployment — reviewing approach and code, not just output.
• Partner with data engineering (or own it directly, where needed) to build and maintain the pipelines, data models, and infrastructure that feed analytics and ML solutions.
• Work with software/product engineering teams to get models and analytical tools into production — comfortable enough with full-stack and Python-based development to prototype APIs, internal tools, or lightweight applications independently.
• Mentor and upskill junior analysts and data scientists; review their work and set the technical bar for the team.
• Contribute to business development — shaping proposals, scoping new engagements, and estimating effort.
• Be comfortable with ambiguity, and enjoy structuring solutions for messy, large-scale (Big Data) problems where the “right” approach isn’t obvious upfront.
What We’re Looking For
Must Have
• 5-8 years of experience in analytics, data science, or a related field, with at least some experience leading projects or mentoring junior team members.
• Strong hands-on skills in Python and SQL; comfortable working across the analytics stack, not just in notebooks.
• Proven experience with end-to-end ML model development — problem framing, feature engineering, model selection, hyperparameter tuning, validation, and deployment — with solid working knowledge of algorithms such as Random Forest, XGBoost, and SVM.
• Familiarity with MLOps practices — model monitoring, versioning, and CI/CD for ML — and experience taking models from prototype to production.
• Practical, hands-on experience with AI/LLMs — working with providers such as OpenAI and Anthropic or open-source alternatives, including prompt engineering (zero-shot, few-shot, chain-of-thought) and building LLM-powered features or workflows.
• Working knowledge of data engineering fundamentals — building and maintaining data pipelines, writing efficient ETL/ELT, and working with cloud data platforms (e.g., AWS/GCP/Azure) and tools such as Airflow or Spark.
• Strong communication and stakeholder management skills — able to translate technical findings into clear, actionable recommendations for business audiences.
Good to Have
• Prior experience with NLP on unstructured or semi-structured data, and familiarity with Hugging Face / Transformers for accessing and deploying pre-trained models.
• Exposure to modern front-end frameworks (React or similar) for building internal analytics tools or dashboards.
• Prior experience in financial services or other regulated industries, or on similarly data-intensive analytics engagements.
Educational Qualification
• Bachelor's or Master's degree in Statistics, Data Science, Engineering, Computer Science, or a related quantitative field from a Tier 1 or Tier 2 institute; management degree (MBA or equivalent) is a plus.
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