Architect, Data Science

Founded in 2017, Lovelytics has helped some of the most complex enterprises across industries such as Energy & Utilities, Media & Entertainment, Manufacturing and Retail & CPG, modernize their data and put AI to work, not in a deck, but in production. We move fast, stay close to the cutting edge, and care more about outcomes than optics.

Our work spans the full data and AI lifecycle:
Data Strategy & Advisory, Data Engineering, Analytics & Visualization, Generative AI & ML, and Cloud Migration & Modernization — all delivered on Databricks, with deep partnerships across AWS, Azure, and GCP. To date, we've delivered over $2B in business value for Fortune 500 clients.

In the last year, Lovelytics as grown from 85 to 500+ people across the Americas, all while keeping the technically excellent, low-ego culture that makes the work worth doing, earning us a Best Places to Work recognition and a spot among Databricks' top partners.

Lovelytics is seeking a Solutions Architect – Data Science with hands‑on experience delivering client‑facing, enterprise‑grade machine learning and advanced analytics solutions, particularly on the Databricks platform. As part of our growing Data Science practice, this role offers an exciting opportunity to lead technical strategy, design end‑to‑end ML pipelines, and drive pre‑sales engagements from initial scoping to final delivery.

The Solutions Architect will play a role in defining business‑value metrics, shaping client proposals, and directing cross‑functional delivery teams to build scalable, high‑impact AI/ML applications. Our ideal candidate blends deep technical expertise across predictive modeling and modern LLM frameworks with seasoned consulting acumen to influence both technical execution teams and C‑suite executives.

Note: This position is not eligible for work sponsorship at this time.

• 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 ML 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.
• Build rapid prototypes, technical pre‑sales presentations, and proof of concepts for prospective client engagements.

• B.S. or M.S. in Computer Science, Engineering, Economics, or a related quantitative field.
• 7+ years of experience in Data Science & AI/ML, including large‑scale solution deployments.
• 4+ years in a client‑facing role, preferably in a professional services firm.
• Proven track record designing and implementing modern data science solutions in at least two of the following areas: forecasting, anomaly detection, recommendation systems, computer vision, propensity modeling, and mathematical optimization.
• Expert knowledge of Python and 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 Transformers, Lang Chain, etc.
• Experience with rapid prototyping and creating proofs of concept, technical pre‑sales presentations, and pricing for engagements.
• Strong client‑facing communication skills with the ability to influence technical and executive stakeholders.
• Prior experience with fraud detection, risk…