Solution Architect, Data Science

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.

• →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 presales 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, LangChain, etc.
• Experience with rapid prototyping and creating proofs of concept, technical presales 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 modeling, or anomaly prevention systems.

• Meaningful, cutting-edge projects in Generative AI with clients across industries—from Fortune 100 firms to disruptive startups.
• Rapid learning and mentorship opportunities with AI/GenAI thought leaders and practitioners.
• A culture of experimentation, innovation, and continuous improvement.
• Opportunity to be a conference speaker, blogger, writer. 
• A diverse, inclusive team and one of the best GenAI teams, where your voice, ideas, and

The base salary range for this position for candidates based in the US is $155,000-190,000 per year. Compensation is determined based on a variety of factors including, but not limited to, relevant experience, skills, qualifications, scope of role and budget. Candidates are not guaranteed to be placed at any particular point within the range, and most offers will fall within the middle of the range or below. Lovelytics is committed to pay equity and transparency in compensation practices.