Data Scientist
Summary
Fully remote (U.S.) Data Scientist on a federal program at Improvix Technologies, supporting a live Splunk-to-Databricks migration. Day to day: building Databricks/PySpark pipelines, Power BI data models and DAX dashboards, Python/SQL automation, and API integrations, with ML modeling and stakeholder-facing analytics. Active Secret clearance and U.S. citizenship required.
Data Scientist
Improvix Technologies | Federal Program | Fully Remote
Compensation: $140,000 – $170,000
Location: Fully Remote (U.S.)
Clearance: Active Secret clearance required. Secret-cleared candidates may be considered for sponsorship to a higher level.
Employment Type: Full-Time
WE'RE MID-MIGRATION, AND WE'RE NOT GOING TO PRETEND OTHERWISE
Most analytics job postings describe a tidy environment — a modern platform, clean data, a dashboard backlog waiting for someone talented. Here's ours instead: we are partway through moving from Splunk to Databricks. Some data lives in one, some in the other, and some reporting still runs on the old path because nobody has rebuilt it yet.
Improvix Technologies is hiring a Data Scientist to work across both platforms while that migration finishes, and to build what comes after. You'll stand up pipelines in Databricks, build the Power BI models and dashboards people actually make decisions from, and write the Python, SQL, and automation that connects it all.
This is end-to-end analytics work — preparation, transformation, modeling, validation, reporting, and production support. You'll partner directly with business stakeholders, and you'll own what you ship after it ships. Fully remote.
WHY DATA SCIENTISTS TAKE THIS ROLE
- Genuinely remote, genuinely cleared. A cleared federal analytics role you can do from anywhere in the U.S. is rare. That's not a perk we bolted on — it's how the program runs.
- A migration you get to shape. You're not inheriting a finished platform and maintaining it. What moves, how it gets modeled, and what gets rebuilt rather than ported are live questions, and you'll have a real say in the answers.
- A clearance upgrade on the table. If you hold a Secret today, we may be able to sponsor you to a higher level. That clearance follows you for the rest of your career.
- AI adoption is wanted, not resisted. The team is actively looking to expand how it uses AI in analytics workflows. Bringing those tools into the work is encouraged here, not something you have to argue for first.
- End-to-end ownership. Pipeline to dashboard to production support. You'll watch your own work get used, and you'll hear about it when it breaks — which is the only reliable way to get good at this.
WHAT YOU'LL OWN
Data Engineering & Pipelines
- Build scalable data pipelines and analytics workflows in Databricks — PySpark, Delta Lake, and notebooks.
- Work with large, complex datasets and find the improvements that matter: query performance, processing time, and overall efficiency.
- Optimize data models for reliability, governance, and reuse across teams, so the next person doesn't rebuild what you already solved.
- Partner with engineering to deploy analytics products into production environments.
Analytics & Modeling
- Perform exploratory data analysis, feature engineering, and model development using machine learning techniques.
- Write the Python that does the analysis, transformation, and automation — and the advanced SQL that queries, transforms, and optimizes against large datasets.
- Validate your own work rigorously enough that stakeholders can trust a number without checking it themselves.
Power BI & Reporting
- Develop interactive dashboards and reports in Power BI with real attention to usability, accuracy, and performance.
- Build and maintain Power BI data models and DAX measures that support both reporting and deeper analysis.
- Document processes, data definitions, and system logic so the reporting stays transparent and maintainable after you move on to the next thing.
Automation & Integrations
- Build workflow automations in Power Automate — a core day-to-day tool for this team, not an occasional one.
- Build and maintain API integrations with external platforms to support data ingestion, including OAuth 2.0 authentication and token management.
- Bring AI tooling into analytics workflows where it genuinely helps, and be clear-eyed about where it doesn't.
Migration Support
- Support the active migration from Splunk to Databricks, working across both platforms as the transition proceeds.
- Help decide what gets ported as-is, what gets rebuilt properly, and what quietly gets retired because nobody has opened it in two years.
Stakeholder Partnership
- Partner with business stakeholders to translate analytical needs into technical solutions — including pushing back when the question behind the request is the wrong one.
- Present findings conversationally rather than by reading slides, and adjust the technical depth for the room — a highly technical audience one hour, non-technical leadership the next.
- Manage competing priorities across cross-functional teams without letting the quiet, important work slide.
HOW SUCCESS IS MEASURED
- Work that lands. Dashboards and analytics that are high quality and have measurable business impact — not just delivered, but used.
- Accessibility and reliability. Well-structured models and pipelines that make data easier to get to and more dependable when you get there.
- Trusted advisor status. Stakeholders come to you for data-driven insight because your answers have held up before.
- Confidence in the reporting. Strong validation, documentation, and data quality practice, so nobody has to ask whether a number is right.
WHAT YOU BRING
These are the requirements — everything in “Bonus Points” below is genuinely optional. If you meet this list, apply.
- 3+ years in analytics, data engineering, or data science roles, or equivalent practical experience.
- Strong Power BI proficiency: data modeling, DAX, report development, and visualization best practices.
- Strong experience with Databricks and distributed data processing in Spark.
- Advanced SQL — querying, transforming, and optimizing large datasets.
- Strong Python for data analysis, transformation, automation, and analytics.
- Power Automate experience for building workflow automations. This is a core day-to-day tool for the team.
- Experience building and maintaining data pipelines and working with modern data architectures.
- Understanding of cloud data platforms and architectures, preferably Microsoft Azure.
- Strong presentation skills — able to speak conversationally about projects, findings, and solutions rather than reading from slides, and to adjust technical depth for both technical stakeholders and non-technical leadership.
- A strong analytical mindset, with the ability to interpret data trends and communicate them clearly.
- Experience collaborating across cross-functional teams and managing competing priorities.
- The adaptability to solve problems with the tools actually available, rather than the ones you'd pick in a greenfield.
- Familiarity with AI applications and their limitations in data science contexts, and experience integrating AI into daily work.
- Bachelor's degree in Data Science, Computer Science, Statistics, Information Systems, or a related field preferred — but not required if your relevant experience is strong.
- U.S. citizenship and an active Secret clearance.
BONUS POINTS
- Splunk experience — search, log analysis, operational analytics, or dashboard development. This is the one we'd weight most heavily: the team is mid-migration, and comfort moving between Splunk and Databricks is a significant advantage.
- Interest or experience applying AI tools inside analytics workflows, such as Power Automate AI Builder or similar AI-assisted platforms.
- Experience developing analytic solutions with models from OpenAI, Anthropic, or similar.
- Familiarity with CI/CD workflows, DevOps practices, or automation tooling.
- Exposure to data governance frameworks or DataOps methodologies.
- Exposure to UiPath or other RPA platforms, and familiarity with ServiceNow for accessing hardware and other operational datasets.
- Experience using Jira for task management and reporting.
- Prior experience supporting federal customers or working in a cleared environment.
WORKING HERE
Improvix Technologies delivers secure, modern, integrated technology solutions that keep the nation's most critical systems running. We're small enough that a good idea reaches a decision-maker in a day, and close enough to the mission that you'll know exactly what your work supports.
On remote work: this role is fully remote within the U.S., and we mean it — no quiet expectation that you'll relocate in six months, and no “remote for now.” You'll need to be reachable during East Coast business hours to stay in sync with the team, and that's the extent of it.
An honest word about the migration: mid-migration environments are messy. You'll hit reporting that depends on a Splunk query nobody documented, and datasets that exist in two places with two answers. If that sounds like an irritation keeping you from the real work, this will be a long year. If it sounds like the work, you'll do well here.
What this role actually weighs: the title says Data Scientist, and there is genuine modeling and feature engineering in it. But a large share of the week is analytics engineering — Databricks pipelines, Power BI data models and DAX, SQL optimization, and Power Automate flows. We'd rather say that plainly than have you discover it in month two.
Ready to apply? Send us your résumé. If there's a pipeline you made fast, a dashboard people actually opened, or a migration you got through without losing anyone's trust in the numbers, tell us about it — that's the conversation we want to have.
Why Improvix
At Improvix Technologies, we don't just deliver IT solutions — we deliver mission success. Our teams support critical government programs by building secure, innovative, and scalable technology that enables agencies to operate more efficiently and securely.
We believe great technology starts with great people. That's why we invest in our employees' growth, well-being, and long-term success. Our benefits include:
• Comprehensive medical, dental, and vision insurance
• 401(k) with company match
• Three weeks of paid annual leave that increases with tenure
• Life insurance and disability coverage
• Transit and commuting benefits
• Professional development and paid training opportunities
• Performance-based incentives
At Improvix, you'll join a collaborative, mission-focused environment where your expertise drives meaningful impact.
Pay Range Statement
The compensation range above reflects the minimum and maximum base salary Improvix in good faith expects to pay for this position at the time of posting. The final offer will depend on factors such as job-related skills, experience, certifications, clearance level and contract requirements. Improvix does not ask for or consider an applicant's salary history.
Equal Opportunity Employer
Improvix Technologies, Inc. is an Equal Opportunity Employer and is committed to building a diverse and inclusive workplace. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, protected veteran status, or any other characteristic protected by federal, state, or local law.
Improvix is committed to providing reasonable accommodations to qualified individuals with disabilities during the application and employment process. If you require assistance or accommodation, please contact our Human Resources team.
Security Clearance / Citizenship Requirements
Due to the nature of our government contracts, U.S. citizenship may be required for certain positions, and some roles may require the ability to obtain and maintain a U.S. government security clearance.
Pay Transparency Statement
Improvix Technologies will not discharge or otherwise discriminate against employees or applicants for discussing, disclosing, or inquiring about their own compensation or the compensation of others.
Skills
- Accessibility
- AI
- Ai Enablement
- Analytics
- Anthropic
- API
- Authentication
- Automation
- Azure
- CI/CD
- Cloud
- Data Engineering
- Data Governance
- Data Ingestion
- Data Modeling
- Data Pipelines
- Data Quality
- Data Science
- Databricks
- DataOps
- Delta Lake
- DevOps
- Feature Engineering
- Jira
- Machine Learning
- OAuth
- OpenAI
- Power Automate
- Power BI
- PySpark
- Python
- RPA
- ServiceNow
- Spark
- Splunk
- SQL
- Statistics
- UiPath