Senior Data Scientist

Amivero•,

About The Position

Amivero’s team of IT professionals delivers digital services that elevate the federal government, whether national security or improved government services. Our human-centered, data-driven approach is focused on truly understanding the environment and the challenge, and reimagining with our customer how outcomes can be achieved. Our team of technologists leverage modern, agile methods to design and develop equitable, accessible, and innovative data and software services that impact hundreds of millions of people. As a member of the Amivero team you will use your empathy for a customer’s situation, your passion for service, your energy for solutioning, and your bias towards action to bring modernization to very important, mission-critical, and public service government IT systems.

Requirements

US Citizenship Required to obtain a Public Trust
DHS ICE Public Trust perferred
Customer-facing experience or the ability to work directly with customers and stakeholders.
Hands-on experience as a Data Scientist, Machine Learning Engineer, AI Specialist, or similar role.
Experience using LLMs and AI models to analyze, summarize, classify, extract, or enhance data.
Strong understanding of statistical analysis and statistical modeling.
Experience finding patterns in data and translating findings into usable insights or business actions.
Strong Python skills and experience with common data science libraries such as pandas, NumPy, scikit-learn, statsmodels, or similar.
Experience working with large datasets and complex data environments.
Strong SQL skills and ability to query, join, transform, and analyze data.
Experience with machine learning techniques such as classification, regression, clustering, anomaly detection, and recommendation methods.
Ability to prepare, validate, and structure data for analysis and modeling.
Understanding of model evaluation, validation, performance metrics, and tuning.
Ability to work with both structured and unstructured data.
Strong communication skills and ability to explain technical concepts to business and technical audiences.

Nice To Haves

Azure AI Studio experience is highly desirable
Databricks experience is strongly preferred.
Experience with Databricks for data science, machine learning, notebooks, workflows, Spark, or Lakehouse environments.
RAG/ semantic search experience
Experience with Azure AI Studio, OpenAI, OpenAI models, Hugging Face, or other LLM platforms.
Experience with prompt engineering, retrieval-augmented generation, embeddings, vector search, or semantic search.
Experience using MLflow, Feature Store, Model Serving, or Mosaic AI capabilities.
Familiarity with Unity Catalog, data governance, lineage, and secure model/data access.
Experience building AI-assisted data discovery, data classification, or data enrichment solutions.
Experience with MLOps, MLflow, CI/CD for models, experiment tracking, and model monitoring.
Experience with NLP, document intelligence, information extraction, or knowledge mining.
Experience in consulting, professional services, or customer-facing delivery.
Relevant certifications in Azure AI, Databricks, machine learning, or data science are a plus.

Responsibilities

Work directly with customers and stakeholders to gather requirements, explain findings, and deliver practical recommendations.
Use LLMs, machine learning models, and statistical methods to identify patterns, trends, anomalies, and relationships within data.
Transform raw, messy, or complex data into usable, meaningful, and business-ready information.
Develop models, prompts, workflows, and analytical approaches that support data discovery, classification, enrichment, and decision-making.
Apply statistical analysis, hypothesis testing, regression, clustering, classification, and other analytical techniques.
Work with structured, semi-structured, and unstructured data, including text, documents, logs, and large datasets.
Use AI and LLM capabilities to extract meaning from data, summarize content, classify information, generate insights, and automate data understanding.
Partner with data engineers to support feature engineering, data preparation, data quality, and model-ready datasets.
Build, test, evaluate, and improve machine learning and AI models.
Develop reusable analytical assets, notebooks, experiments, and data science workflows.
Support the deployment and operationalization of models into production environments.
Build frameworks to evaluate and continuously monitor model performance, accuracy, reliability, bias, drift and business usefulness.
Collaborate with architects and engineers on AI-enabled data platforms and Lakehouse solutions.
Present technical results in a clear and business-friendly manner.
Document methodologies, assumptions, models, data sources, and outputs.