AI/ML Job Openings At KPMG – Apply Soon!

Qualifications

• Bachelor of Science or Bachelor of Engineering at a minimum.
• 2+ years of work experience as a Data Scientist
• A combination of business focus, strong analytical and problem-solving skills, and programming knowledge to be able to quickly cycle hypothesis through the discovery phase of a project
• Advanced skills with statistical/programming software (e.g., R, Python) and data querying languages (e.g., SQL, Hadoop/Hive, Scala)
• Good hands-on skills in both feature engineering and hyperparameter optimization
• Experience producing high-quality code, tests, documentation
• Experience with Microsoft Azure or AWS data management tools such as Azure Data factory, data lake, Azure ML, Synapse, Databricks
• Understanding of descriptive and exploratory statistics, predictive modelling, evaluation metrics, decision trees, machine learning algorithms, optimization & forecasting techniques, and / or deep learning methodologies
• Proficiency in statistical concepts and ML algorithms
• Good knowledge of Agile principles and process
• Ability to lead, manage, build, and deliver customer business results through data scientists or professional services team
• Ability to share ideas in a compelling manner, to clearly summarize and communicate data analysis assumptions and results
• Self-motivated and a proactive problem solver who can work independently and in teams

Must to Have: Agent Framework, RAG Framework, Chunking Strategies, LLMs, AI on cloud Services, Open Source Frameworks like Langchain, Llama Index, Vector Database, Token Management, Knowledge Graph, Vision APIs, Prompt Engineering

Good to have: AI Algorithms, Deep Learning,Computer Vision, Hallucination Control Mechanism, Responsible AI frameworks
Tech : Python, SQL, Docker, Versioning tool, Azure/AWS/GCP

Major Duties & Responsibilities:

• Work with business stakeholders and cross-functional SMEs to deeply understand business context and key business questions
• Create Proof of concepts (POCs) / Minimum Viable Products (MVPs), then guide them through to production deployment and operationalization of projects
• Influence machine learning strategy for Digital programs and projects
• Make solution recommendations that appropriately balance speed to market and analytical soundness
• Explore design options to assess efficiency and impact, develop approaches to improve robustness and rigor
• Develop analytical / modelling solutions using a variety of commercial and open-source tools (e.g., Python, R, TensorFlow)
• Formulate model-based solutions by combining machine learning algorithms with other techniques such as simulations.
• Design, adapt, and visualize solutions based on evolving requirements and communicate them through presentations, scenarios, and stories.
• Create algorithms to extract information from large, multiparametric data sets.
• Deploy algorithms to production to identify actionable insights from large databases.
• Compare results from various methodologies and recommend optimal techniques.
• Design, adapt, and visualize solutions based on evolving requirements and communicate them through presentations, scenarios, and stories.
• Develop and embed automated processes for predictive model validation, deployment, and implementation
• Work on multiple pillars of AI including cognitive engineering, conversational bots, and data science
• Ensure that solutions exhibit high levels of performance, security, scalability, maintainability, repeatability, appropriate reusability, and reliability upon deployment
• Lead discussions at peer review and use interpersonal skills to positively influence decision making
• Provide thought leadership and subject matter expertise in machine learning techniques, tools, and concepts; make impactful contributions to internal discussions on emerging practices
• Facilitate cross-geography sharing of new ideas, learnings, and best-practices