Altimetrik delivers outcomes for our clients by rapidly enabling digital business & culture and infuse speed and agility into enterprise technology and connected solutions. We are practitioners of end-to-end business and technology transformation. We tap into an organization’s technology, people, and assets to fuel fast, meaningful results for global enterprise customers across financial services, payments, retail, automotive, healthcare, manufacturing, and other industries. Founded in 2012 and with offices across the globe, Altimetrik makes industries, leaders and Fortune 500 companies more agile, empowered and successful.
Altimetrik helps get companies get “unstuck”. We’re a technology company that lives organizations a process and context to solve problems in unconventional ways. We’re a catalyst for organization’s talent and technology, helping teams push boundaries and challenge traditional approaches. We make delivery more bold, efficient, collaborative and even more enjoyable.
About the Role
We are seeking a Data Scientist to frame problems, run experiments, and develop models that turn data into actionable insight. Partnering with our Solution Architects, AI/ML Engineers, and stakeholders, you'll work from the earliest discovery conversations through analysis and modeling — helping shape what gets built and proving out what works.
The ideal candidate is intellectually curious and pragmatic: someone who can ramp up quickly on a new domain, ask sharp questions during discovery, and move from hypothesis to validated insight fast in an iterative, rapid-development environment. You're equally comfortable digging into messy data, designing a rigorous experiment, and explaining results clearly to a non-technical audience.
What You'll Do
- Frame business problems as analytical and ML problems, defining hypotheses, success metrics, and the right modeling approach.
- Engage proactively during discovery, asking incisive questions, assessing data readiness, and identifying opportunities and risks early.
- Perform exploratory data analysis to understand data quality, distributions, relationships, and feasibility before modeling.
- Design and run experiments (A/B tests, statistical analyses) with appropriate rigor, and interpret results to drive decisions.
- Develop, validate, and iterate on models — from statistical and classical ML approaches through to modern AI/GenAI techniques where appropriate.
- Jumpstart contributions immediately by ramping quickly on new domains and datasets and delivering early, meaningful analysis.
- Support rapid, iterative development — prototype quickly, validate assumptions, and partner with engineers to move promising work toward production.
- Communicate findings clearly through visualizations, narratives, and recommendations tailored to technical and business audiences.
- Collaborate across the team with architects, engineers, and stakeholders to keep analysis aligned with evolving goals.
- Champion rigor and responsible AI — sound methodology, reproducibility, fairness, and clear articulation of assumptions and limitations.
What You Bring
Required Qualifications
- 5+ years of experience applying data science and machine learning to real-world problems.
- Strong foundation in statistics, experimental design, and machine learning methods.
- Proficiency in Python (and/or R) and SQL, with experience in common data science libraries.
- Hands-on experience with the modeling lifecycle: problem framing, EDA, feature engineering, model development, and evaluation.
- Proven ability to ramp quickly and deliver insight in fast-moving, ambiguous environments.
- Excellent communication and data storytelling skills, including the ability to explain complex results simply.
- Authorized to work in the United States.
Technical Skills
- Languages & libraries: Python (pandas, NumPy, scikit-learn), SQL; R a plus.
- ML & statistics: regression, classification, clustering, time series, hypothesis testing, experimental design.
- Visualization: tools such as matplotlib, seaborn, Plotly, Tableau, or Power BI.
- Platforms: experience with cloud data/ML environments (AWS, Azure, GCP, Databricks) and notebooks.
- GenAI (plus): familiarity with LLMs, embeddings, and RAG approaches for analytical use cases.
Preferred Qualifications
- Experience in a consulting or client-facing delivery environment.
- Advanced degree in a quantitative field (Statistics, CS, Math, Economics, or similar) or equivalent experience.
- Experience with experiment tracking (MLflow, Weights & Biases) and collaborating with engineers to productionize models.
- Domain experience relevant to our clients.
