SENIOR MANAGER, SOLUTION CONSULTING

Job description

**Location: Bangalore** Help shape the future of Finance at Walmart by applying data science and Generative AI to high-value finance workflows. In this role, you’ll partner across Finance, Engineering, and business teams to design, build, and productionize models (forecasting, anomaly detection, NLP/LLM, optimization) that streamline close, planning, and controllership processes. You will also empower associates to build personal AI agents and guide the model lifecycle—from problem framing and data pipelining to experimentation, evaluation, deployment, and monitoring—advancing Walmart’s broader Generative AI strategy. If you thrive at the intersection of finance, GenAI, and business transformation, this role is for you. **About Finance Technology & Transformation** The Finance team drives efficiency, accuracy, and innovation across Walmart’s financial operations. Our mission is to equip associates with the data, models, and intelligent automation needed to operate effectively and reach the company’s strategic goals. This role is critical to accelerating our Data Science and GenAI adoption—not just piloting technology, but reimagining how finance teams work with trustworthy, scalable AI systems. **What you’ll do** - Identify and frame data science opportunities across close, reporting, planning, controllership, and treasury (e.g., journal entry automation, variance root-cause analysis, forecasting, outlier detection, risk scoring, exception triage). - Build and productionize models (e.g., time series forecasting, classification, regression, NLP/LLM, clustering, optimization) with strong MLOps discipline: data versioning, CI/CD, model registry, monitoring, drift detection, retraining. - Develop GenAI solutions for finance knowledge retrieval and decision support: RAG pipelines, vector stores, prompt orchestration, evaluation frameworks, and guardrails for privacy, PII, and compliance. - Stand up robust data pipelines with Finance and Engineering (batch/stream) that ensure data quality, lineage, and governance; collaborate on feature stores and reusable semantic layers. - Run structured experimentation (A/B tests, quasi-experiments, backtesting) and partner on measurement frameworks to quantify uplift, precision/recall, cycle-time improvements, and ROI. - Enable the business: coach analysts and finance partners to build no/low-code copilots, reusable notebooks, and light automations; create reference patterns for repeatable use cases. - Contribute to the Generative AI strategy for Finance: use-case portfolio, value mapping, roadmap, standards for responsible AI, and integration with enterprise platforms. - Partner on risk & controls: ensure models meet SOX-relevant controls, explainability thresholds, and documentation standards; collaborate with Legal, Privacy, and InfoSec. - Be a mentor to the team members and develop an atmosphere of trust and openness - Manage a team of data scientists and solution consultants **What you’ll bring** - Finance fluency: hands-on understanding of FP&A, controllership, and accounting workflows; ability to translate finance pain points into well-scoped ML problems. - Data science depth: strong skills in Python and SQL; experience with pandas/Polars, scikit-learn, PyTorch or TensorFlow; methods including time series, causal/experiment design, classification/regression, anomaly detection, clustering, optimization, and NLP/LLMs. - GenAI & LLM ops: experience with prompt engineering, evaluation, RAG architectures, embeddings/vector DBs, and running LLM apps with guardrails and observability. - MLOps & data engineering: familiarity with Databricks/Snowflake/Spark, MLflow/model registry, Feature Store, GitHub Actions/CI-CD, containerization, and production monitoring. - Data governance & controls: understanding of data lineage, metadata management, privacy/PII, access controls, and model governance (documentation, approvals, and quality gates). - Product mindset: ability to frame business value, prioritize a portfolio, tell the story with metrics, and drive adoption with cross-functional teams. - Collaboration: proven track record working with Finance, Engineering, and Product to deliver production systems—not just POCs. **Nice to have (preferred)** - Experience with financial systems (e.g., SAP/S4, Anaplan, consolidation/reporting tools) and common finance data models. - Familiarity with GenAI platforms (e.g., Microsoft Copilot/LLM Studio, Azure OpenAI, Vertex AI, Bedrock). - Knowledge of vector databases (FAISS, Milvus, pgvector) and observability tools (Arize, Fiddler, Evidently). - Experience implementing model explainability (SHAP, feature importance) and policy guardrails for regulated workflows. - Background in optimization (LP/MIP) for allocation/scheduling/cash optimization. **Position Requirements:** Minimum qualifications: - Overall 12–15+ years of experience in AI/ML, analytics, software engineering, or related technical fields - Strong understanding of contemporary AI/LLM systems: - Prompting techniques and optimization strategies - Grounding strategies for factual accuracy - Enterprise AI solution design patterns - Experience working with large datasets and data informed systems - Strong SQL skills for validation, analysis, and optimization - Exposure to Business Intelligence (BI) and analytical ecosystems - Excellent communication skills for diverse audiences - Strong stakeholder partnership and cross functional collaboration skills - Self starter mindset with curiosity, adaptability, and comfort navigating ambiguity - Experience of managing a high powered team Additional Qualifications: - Experience applying AI solutions in Finance or large enterprise environments - Familiarity with enterprise AI governance and responsible AI practices - Ability to evaluate and improve end to end quality of AI solutions - Experience debugging or enhancing AI generated or rapidly prototyped applications (including “vibe coded” solutions) - Knowledge of