Enterprise Data Science Architect — AI/ML & Pre-Sales Leader

Lovelytics

Arlington (VA)

On-site

USD 145,000 - 200,000

Full time

14 days+

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Job summary

Lovelytics, an Arlington, VA-based data science consultancy, is seeking a Data Scientist Architect to lead client-facing, enterprise-grade ML/AI solutions on Databricks. You will design end-to-end pipelines and guide cross-functional delivery teams through data analysis, model training, deployment and MLOps.

The ideal candidate has 7+ years in data science/AI, strong Python/SQL skills, and hands-on experience with Databricks.

Qualifications

Bachelor’s or Master’s degree in Computer Science, Engineering, Statistics, economics or a related field.
7+ years of experience in data science & AI/ML, including large-scale solution deployments.
5+ years in a client facing role, preferably in a professional services firm.
Strong communication skills with the ability to influence technical and executive stakeholders.
Proven track record designing and implementing modern data science solutions in at least two of the following areas: forecasting, anomaly detection, recommendation system, computer vision, risk modeling or mathematical optimization.
Expert knowledge of Python, SQL and strong hands-on experience with Databricks (Unity Catalog, MLFlow, Delta Tables, etc.).
Proficiency with commercial and open-source LLM APIs and tooling like Hugging Face, LangChain, DSPY etc, as well as coding assistants.
Experience with rapid prototyping and creating proofs of concept, technical presales presentations and pricing for engagements.
Prior experience with revenue management, price modeling and causal Inference.

Responsibilities

Define clear business-value metrics (e.g., ROI, revenue impact, cost reduction) alongside technical performance targets for every engagement.
Lead technical scoping, feasibility assessments and solution design for proposals, Statements of Work and client pitches.
Design scalable, secure and maintainable end-to-end multi-layer ML/AI pipelines, spanning data analysis, feature engineering, model training, deployment and MLOps.
Direct cross-functional delivery teams of Data Scientists, Data Engineers and ML Engineers to execute solutions aligned with architectural standards.
Establish code quality standards, model validation protocols and technical best practices to prevent technical debt and ensure system reliability.
Articulate technical concepts, model trade-offs and operational risks clearly to both technical teams and non-technical C-suite stakeholders.
Design operational workflows and user integration strategies to drive high adoption of AI/ML tools among end-users.
Conduct client enablement workshops and knowledge transfer sessions to build internal capability and ensure long-term solution sustainability.