Data Scientist - Associate Manager

Accenture helps the world’s leading enterprises reinvent by building their digital core and unleashing the power of AI to create value at speed for organizations across industries. Our strategy is to be the reinvention partner of choice for our clients and lead in the safe, widespread adoption of AI, and to be the most client-focused, AI-enabled, great place to work in the world. We bring together the talent of our approximately 799,000 people with proprietary assets and platforms, deep process and industry expertise, and leading ecosystem relationships to deliver end-to-end solutions and measurable outcomes at scale. Through our Reinvention Services, we offer broad expertise across Cybersecurity, Digital Core, Finance, Industry and Enterprise, Song, Supply Chain and Engineering, and Talent, with advanced capabilities in AI and Data, Industry and Process, and Technology. We serve approximately 9,000 clients and generated approximately $70 billion in FY25 revenue. Visit us at accenture.com . Role Summary Lead a small data science team and own client-facing delivery across multiple workstreams. Remain hands-on with complex modeling, experimentation, validation, production deployment, and model-governance activities.

Key Responsibilities- Lead a small team of data scientists, including work assignment, coaching, performance management, and professional development.

• Scope, estimate, prioritize, and monitor data science workstreams based on business impact, delivery risk, and available capacity.
• Translate client business needs into data science use cases, analytical approaches, delivery roadmaps, and measurable outcomes.
• Lead client discussions covering methodology, assumptions, limitations, trade-offs, validation results, and recommended actions.
• Design, build, and review production-grade machine-learning and statistical models using Python and SQL.
• Review modeling approaches and code, identify methodological issues, and define corrective actions.
• Design and evaluate experiments and causal analyses, documenting assumptions, limitations, and validation evidence.
• Investigate model degradation, failed replication, unexpected experimental results, data-quality issues, and performance gaps.
• Prototype and assess new modeling approaches before broader implementation.
• Oversee model deployment and lifecycle activities, including monitoring, drift detection, retraining, and performance reporting.
• Define and apply model-governance, validation, explainability, fairness, and responsible-AI controls.
• Coordinate risks, dependencies, decisions, and escalations across concurrent technical workstreams. Required Qualifications- 6 to 8 years of applied data science experience, including experience leading data scientists or comparable technical teams.
• Bachelor’s or advanced degree in Data Science, Statistics, Computer Science, Mathematics, Economics, or a related field; equivalent relevant professional experience is accepted.
• Advanced, recent hands-on experience with Python for developing and reviewing machine-learning or statistical modeling code.
• Advanced SQL experience for complex analytical work and data preparation.
• Applied experience with supervised and unsupervised machine-learning methods, including model selection and trade-off assessment.
• Experience designing or auditing experiments and causal-inference analyses.
• Experience with model validation, including calibration, failure analysis, and alignment to business metrics.
• Experience deploying models to production and managing monitoring, drift detection, retraining, or related lifecycle activities.
• Experience with at least one cloud machine-learning platform, such as Azure Machine Learning, AWS SageMaker, Google Vertex AI, or an equivalent platform.
• Experience with MLOps practices, such as experiment tracking, feature stores, model registries, or CI/CD for machine-learning models.
• Experience implementing or maintaining model-governance, validation, explainability, fairness, or responsible-AI controls.
• Client-facing experience presenting technical methodologies, findings, limitations, and recommendations to stakeholders.
• Experience planning and coordinating multiple data science workstreams. Preferred Qualifications- Production experience with deep-learning frameworks such as PyTorch, TensorFlow, or an equivalent framework.
• Applied experience with large language models or natural-language processing, including RAG architectures, evaluation frameworks, or prompt-engineering solutions.
• Experience with uplift modeling, reinforcement learning, or advanced recommender systems.
• Experience with Spark or an equivalent distributed-processing framework for large-scale feature or training pipelines.
• Experience with containerized or orchestrated machine-learning workflows.
• Experience with Looker, Power BI, Tableau, or an equivalent analytics and semantic-layer platform.
• Experience in marketing funnel, CRM, commercial analytics, or experimentation-platform ownership.
• Cloud machine-learning or advanced data science certification from a recognized provider.
• Experience with Agile delivery at scale and technical documentation standards. What We Offer- Health and Life Insurance
• Accenture days, 3 additional vacation days
• On site doctor
• Birthday leave
• Internet reimbursement