Senior Data Science Architect

Job Description

Senior Technical Architect Data Science & Agentic AI

Reports to: Engagement Manager / Associate Partner (project-based)

Employment Type: Full-time

About us

We turn customer challenges into growth opportunities.

Material is a global strategy partner to the world's most recognizable brands and innovative companies. Our people around the globe thrive by helping organizations design and deliver rewarding customer experiences.

We use deep human insights, design innovation and data to create experiences powered by modern technology. Our approaches speed engagement and growth for the companies we work with and transform relationships between businesses and the people they serve.

Srijan, a Material company, is a renowned global digital engineering firm with a reputation for solving complex technology problems using their deep technology expertise and leveraging strategic partnerships with top-tier technology partners.

Role summary

- We're seeking a hands-on Sr. Data Science Architect who can lead the end-to-end modeling lifecyclefrom problem framing and experiment design to production deployment and monitoringwhile setting up the technical architecture for ML/GenAI and agentic systems. This is not a data-engineering-heavy role; you'll partner with DE/Platform teams, but your center of gravity is modeling excellence, MLOps, and AI solution architecture that moves business KPIs.

What you'll do

Strategy & Architecture (Data Sciencefirst)

- Own the technical vision for data-science initiatives; translate ambiguous business goals into modellable problems, KPIs, and NFRs/SLOs.
- Define reference architectures for classical ML, deep learning, and agentic GenAI (RAG, tool-use, human-in-the-loop) including model registry, evaluation harness, safety/guardrails, and observability.
- Make build vs. buy and model/provider cho ices (OpenAI/Claude/Gemini vs open-source), including optimization strategies (INT8/4, AWQ/GPTQ, batching, caching).

DS Leadership & Experimentation

- Lead problem decomposition, feature strategy, experiment design (A/B, interleaving, offline/online eval), error analysis, and model iteration.
- Guide teams across NLP, CV, speech, time series, recommendation, clustering/segmentation, and causal/uplift where relevant.
- Establish rigorous quality bars: data & label quality checks, leakage prevention, reproducibility, and statistical validity.

Productionization & MLOps

- Architect CI/CD for models (unit/contract tests, drift checks, performance gates), model registry/versioning, and safe rollouts (shadow, canary, blue-green).
- Design monitoring for accuracy, drift, data integrity, latency, cost, and safety (toxicity, bias, hallucination); close the loop with automated retraining triggers where appropriate.
- Orchestrate RAG pipelines (chunking, embeddings, retrieval policies), agent planning/execution, and feedback loops for continuous improvement.

Stakeholders & Enablement

- Partner with product, strategy/innovation, design, and operations to align roadmaps; run architecture and model review sessions with clear trade-offs.
- Provide technical mentorship to data scientists/ML engineers; codify patterns via playbooks, ADRs, and reference repos.
- Collaborate with Ops/SRE to ensure solutions are operable: runbooks, SLIs/SLOs, on-call, and cost controls.

Governance, Risk & Compliance

- Embed model governance: approvals, lineage, audit trails, PII handling, policy-as-code; support GDPR/ISO/SOC2 requirements.
- Champion human oversight for agentic systems with clear escalation and decision rights.

Must-have qualifications

- 1420 years delivering AI/ML in production, with 5+ years in an architect/tech-lead capacity.
- Expert Python and ML stack (PyTorch and/or TensorFlow), plus strong SQL and software engineering fundamentals (testing, packaging, profiling).
- Proven record architecting scalable DS solutions on AWS/Azure/GCP; hands-on with Docker and Kubernetes(collaborating with platform teams rather than building infra from scratch).
- MLOps proficiency: MLflow/Kubeflow, model registry, pipelines (Airflow/Prefect/Vertex/Bedrock/SageMaker pipelines), feature stores, and real-time/batch serving (KServe/Seldon/Triton/vLLM/Ray Serve).
- Depth across traditional ML and DL (NLP, CV, speech, time-series, recommendation, clustering/segmentation) and the ability to select/prioritize the right approach for the KPI.
- Excellence in communication and stakeholder leadership; experience guiding cross-functional teams (DS, MLE, DE, Product, Ops) to ship value.

Preferred qualifications

- Agentic AI & RAG: LangChain/LangGraph or equivalent orchestration; vector DBs (pgvector, Pinecone, Weaviate, Qdrant); retrieval policy design and evaluation.
- Evaluation & Safety: offline metrics (precision/recall, ROC/PR, BERT-F1, BLEU/ROUGE), LLM eval harnesses, red-teaming, prompt/response guardrails.
- Experimentation: online testing at scale, counterfactual/causal inference, telemetry design.
- Performance & Cost: quantization, speculative decoding, KV caching, batching/collation, throughput tuning on CPU/GPU.
- Familiarity with data-viz/decision support (Tableau/Power BI/D3) and UX/HCI collaboration for human-in-the-loop designs.
- Consulting experience or multi-vendor delivery; pre-sales/SoW exposure.

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