Data Scientist

Data Scientist

Company: Data Science & Engineering Experts, LLC (DSE)

Location: Remote

Job Type: Full-Time

Reports To: Chief Data Officer (CDO) / Chief Technology Officer (CTO)

Compensation: $130,000 - $160,000 annually

About DSE

Data Science & Engineering Experts (DSE) is a pre-seed AI and data systems company building secure, enterprise-grade solutions for complex technical challenges. As a remote-first team, we're establishing both the technical and operational foundation that will scale alongside our innovation.

Our Mission: To transform enterprises through secure, compliant, and measurable AI solutions—bridging data strategy, infrastructure, and applied intelligence to power responsible innovation. We believe in transparency, accountability, and human-centered AI adoption.

The Opportunity

We're seeking a Data Scientist to design, develop, and deploy machine learning models and analytical solutions that drive measurable business outcomes for DSE's enterprise and federal government clients. You'll work across the full data science lifecycle—from problem framing and exploratory analysis to model development, validation, and production deployment.

This is not a traditional data science role where you inherit clean datasets, established pipelines, and well-defined problem statements. This is a foundational 0-to-1 opportunity where you'll establish data science methodologies, build analytical frameworks from scratch, develop reusable modeling patterns, and directly shape how DSE delivers AI/ML value to clients.

As a pre-seed startup, DSE operates with an equity-first compensation model where founding team members invest in the company's success through deferred cash compensation in exchange for meaningful ownership. This role offers significant equity stake aligned with early-stage contribution, with salary activation targeted within 12-18 months upon seed funding based on current investor and client pipeline progress.

You'll partner directly with the CDO, engineering leadership, and client-facing teams to translate complex business problems into actionable analytical solutions. This role requires someone who can seamlessly move between statistical rigor and practical business application—comfortable presenting to executives, writing production-quality code, and mentoring junior analysts as the team scales.

This role is ideal for someone who:

• Wants to build, not inherit—create data science methodologies and modeling frameworks from scratch rather than execute predefined analyses

• Has deep statistical and machine learning expertise with hands-on experience deploying models in production environments

• Thrives in ambiguity and creates structure where none exists

• Sees data science as a strategic enabler for business transformation, not just technical analysis

• Is energized by pre-seed equity compensation in exchange for significant founding-team-level upside

• Holds technical certifications that demonstrate credibility with enterprise customers and data teams

What You'll DoBuild the Foundation (Months 1-6)

• Establish DSE's data science methodology and best practices, including model development frameworks, documentation standards, and code review processes

• Design and implement exploratory data analysis (EDA) frameworks to rapidly assess client data quality, identify patterns, and surface actionable insights

• Develop machine learning models using Python (scikit-learn, TensorFlow, PyTorch, XGBoost) for classification, regression, clustering, and time-series forecasting use cases

• Create reusable feature engineering pipelines and data preprocessing modules that accelerate model development across client engagements

• Implement statistical hypothesis testing, A/B testing frameworks, and experimental design methodologies for data-driven decision-making

• Build model validation and performance monitoring frameworks to ensure model reliability, fairness, and compliance with enterprise requirements

Drive Strategic Execution (Months 6-12)

• Lead end-to-end data science engagements for DSE's first enterprise and federal clients, from problem scoping through model deployment and business impact measurement

• Deploy production ML models using MLOps best practices, including model versioning, automated retraining, and performance drift detection

• Develop natural language processing (NLP) solutions for text classification, entity extraction, sentiment analysis, and document processing applications

• Create compelling data visualizations and executive presentations that translate complex analytical findings into actionable business recommendations

• Collaborate with engineering teams to integrate ML models into client systems, APIs, and data pipelines

• Establish model governance and documentation practices that meet federal compliance requirements (NIST AI RMF, responsible AI principles)

Scale the Organization (Months 12-24)

• Build and mentor a data science team as the organization scales, establishing team standards, knowledge sharing practices, and career development frameworks

• Expand DSE's analytical capabilities to include deep learning, computer vision, reinforcement learning, and generative AI applications

• Drive thought leadership through published research, client workshops, and industry conference presentations that establish DSE's data science credibility

• Develop intellectual property including proprietary algorithms, model architectures, and analytical frameworks that differentiate DSE in the market

• Contribute to business development by supporting technical sales, proposal development, and client presentations

• Prepare data science practice for Series A scale: build capacity planning models, define role ladders, and establish hiring criteria for 25+ employee organization

What We're Looking ForRequired

*Experience & Track Record:*

• 5+ years in Data Science, Machine Learning, or Applied Statistics roles with demonstrated business impact

• 3+ years hands-on experience deploying machine learning models in production environments

• Proven experience working in early-stage (seed to Series A) environments or building data science capabilities from scratch within larger organizations

• Startup DNA: demonstrated ability to operate in resource-constrained, ambiguous environments; comfortable with hands-on execution while thinking strategically

• Experience working with enterprise or federal government clients on data-driven initiatives

*Technical Depth & Certifications:*

• Master's degree in Data Science, Statistics, Computer Science, Mathematics, or related quantitative field (Ph.D. preferred)

• At least ONE of the following certifications (REQUIRED):

• AWS Certified Machine Learning Specialty

• Google Cloud Professional Machine Learning Engineer

• Azure Data Scientist Associate or Azure AI Engineer Associate

• Databricks Certified Machine Learning Professional or Associate

• TensorFlow Developer Certificate or equivalent deep learning certification

• Expert proficiency in Python for data science (pandas, NumPy, scikit-learn, TensorFlow/PyTorch)

• Deep understanding of statistical methods, hypothesis testing, and experimental design

• Experience with SQL and data manipulation across relational and NoSQL databases

• Proficiency with data visualization tools (Matplotlib, Seaborn, Plotly, Tableau, Power BI)

• Experience with cloud-based ML platforms (SageMaker, Vertex AI, Azure ML, Databricks)

*Leadership & Communication:*

• Track record of translating complex analytical findings into executive-level business recommendations

• Excellent documentation skills: can create clear model documentation, technical reports, and reproducible analysis notebooks

• Strong communication skills to collaborate effectively with engineering teams, product managers, and business stakeholders

• Experience mentoring junior data scientists or analysts

Strongly Preferred

*Additional Certifications & Credentials:*

• Multiple cloud ML certifications (AWS, GCP, Azure)

• Databricks Certified Data Engineer Associate or Professional

• Snowflake SnowPro Advanced certifications

• CAP (Certified Analytics Professional) or similar industry credentials

*Domain & Industry Experience:*

• Experience with federal/government compliance requirements (NIST AI RMF, responsible AI frameworks, FedRAMP)

• Healthcare, financial services, manufacturing, or defense sector data science experience

• Experience with MLOps tools (MLflow, Kubeflow, Weights & Biases, DVC)

• Natural language processing (NLP) and large language model (LLM) experience

• Computer vision and deep learning experience

*Operating Model & Tools:*

• Remote-first or distributed team experience with async communication mastery

• Atlassian suite expertise: JIRA for project tracking and Confluence for documentation

• Version control best practices (Git, GitHub/GitLab) for collaborative data science

Nice to Have

• Active security clearance or ability to obtain clearance

• Ph.D. in a quantitative field with published research

• Contributions to open-source data science or ML projects

• Published papers, blog posts, or conference presentations on data science topics

• Experience with causal inference and econometric methods

• Kaggle competitions or similar demonstrable ML achievements

Compensation & BenefitsEquity-First, Deferred Compensation Structure

• Base Salary: $130,000 - $160,000 annually (deferred until seed funding is secured)

• Equity Package: Significant equity stake aligned with early-stage contribution—founding-team-level ownership reflecting the value you create at this stage

• Benefits Upon Funding Activation:

• Health insurance (medical, dental, vision

• 401(k) with matching

• Flexible schedule and unlimited PTO

• Professional development budget (certifications, conferences, courses)

• Home office stipend

• Latest tech tools and software subscriptions

Job Type: Full-time

Pay: $130,000.00 - $160,000.00 per year

Application Question(s):

• Which of the following ML certifications do you currently hold? Select all that apply: AWS Certified Machine Learning Specialty, Google Cloud Professional Machine Learning Engineer, Azure Data Scientist Associate, Azure AI Engineer Associate, Databricks Certified Machine Learning Professional, TensorFlow Developer Certificate, None of the above.
• This role offers equity-first compensation with salary deferred until seed funding (targeted 12-18 months). In 2-3 sentences, describe how this model aligns with your current career goals and financial situation.
• Describe a time you built a data science capability, model, or process from scratch — not inherited or improved an existing one. What ambiguity did you face and how did you create structure?

Work Location: Remote