Sr. Data Scientist

Our Blueprint (Company Overview)

Built for Belonging (Why You'll Love Working Here)

Here’s why you’ll love working here:

• Access to competitive compensation & benefits that reward your contributions.
• Participation in employee recognition programs to celebrate achievements big and small.
• Exposure to professional development & learning initiatives to help you build your own path.
• Foundation built on values anchored in Accountability, Team, Urgency, Respect, Integrity, and Innovation.
• Opportunities for global exposure work alongside talented colleagues across India, Singapore, the UK, and the US, expanding your perspective and network.
• Invitations to yearly team offsites to connect, collaborate, and strengthen relationships in person.
• Leverage a lifestyle spending account benefit so you can choose your own wellness and work-from-home perks.

Our commitment to you:

• Our aspiration: A culture where every person feels confident navigating their unique path and inspired to grow every step of the way.
• Our support: Meeting you where you are, empowering your growth with clarity, support, and purpose.
• Our promise: Your growth is yours to lead, but never a journey you take alone.

Driven to Deliver (What You'll Do)

• Build and productionize ML solutions on AWS: Develop, train, and deploy models using AWS services (e.g., SageMaker, S3, ECR, CloudWatch), with attention to scalability, latency, reliability, and cost.
• Deliver data science as APIs: Design and implement FastAPI (or equivalent) services that expose scoring/inference endpoints; define request/response contracts; implement validation, logging, error handling, and versioning.
• Own the DS-to-production lifecycle: Partner with engineers to operationalize feature pipelines, batch/real-time scoring, A/B tests, and monitoring for drift/quality; contribute to model governance and documentation.
• Operate cross-functionally: Work closely with Product Managers, Engineers, and other stakeholders to translate business goals into DS requirements, define success metrics, prioritize backlogs, and deliver iteratively.
• Raise the bar on DS engineering rigor: Apply software engineering best practices (readable code, modular design, tests, CI/CD collaboration, reproducibility) to research and production codebases.
• Leverage Snowflake for production analytics and ML workflows: Write performant SQL, build/optimize data models, and partner with Data Engineering on feature tables, data quality checks, and scalable access patterns (e.g., secure views, role-based access).

Equipped to Excel (Qualifications)

• Advanced degree (Ph.D. or Master's) plus 4+ years of non-academic professional experience OR Bachelor's degree with 7+ years of professional experience
• AWS fundamentals for ML delivery, including practical experience with AWS SageMaker (training jobs, endpoints/batch transform, model registry or equivalent patterns), S3, IAM basics, and CloudWatch logging/monitoring.
• Experience building and deploying data science inference services using FastAPI (or similar Python API frameworks), including endpoint design, payload validation, and performance considerations.
• Experience collaborating with engineering on productionization patterns (containers, CI/CD, deployment workflows), even if final ownership sits with Engineering/MLOps.
• Strong stakeholder management skills with demonstrated experience working in cross-functional teams (Product, Engineering, Data Engineering, Analytics/BI). 
• Excellent SQL expertise
• Strong background in statistical methods, including: Regression analysis, Model testing, Logistic regression, Generalized Linear Models (GLM) and Solid understanding of statistics and random variables
• Strong applied experience across supervised and unsupervised learning, including GBMs, K-means, neural networks, SVMs, Bayesian models, and NLP, implemented using modern ML frameworks such as TensorFlow, Scikit-learn, PyTorch, and Keras.

Distinct by Design (What Sets You Apart)

• Demonstrated ability to convert ambiguous business problems into measurable DS deliverables (metrics, experiments, and milestones). 
• Strong engineering instincts for production ML (performance, reliability, maintainability), with comfort working alongside engineers on deployment constraints and trade-offs.
• Clear communicator who can drive alignment across DS/Product/Engineering and proactively surface risks, dependencies, and decisions.
• Team player and proven relationship-builder
• Strong interpersonal skills, high level of professionalism and integrity 
• Excellent organizational and project management skills
• Experience handling multiple responsibilities, tasks, and projects in a fast-paced environment preferred
• A positive attitude that approaches tasks/projects from a hands-on, roll up your sleeves frame of mind

Your Next Chapter

If you’re excited about helping clients succeed, love the idea of working at the forefront of marketing innovation, and want to be part of a team where integrity, respect, innovation, urgency, and accountability aren’t just words — they’re how we show up every day — then let’s talk.