KPMG

Technology Consulting Data Science -Consultant

Posted On

April 25, 2025

Location

Gurugram, Haryana

Experience Level

KI Professional

Job Type

Full Time

Apply By

June 18, 2025

Job Description

About KPMG in India

KPMG entities in India are professional services firm(s). These Indian member firms are affiliated with KPMG International Limited. KPMG was established in India in August 1993. Our professionals leverage the global network of firms, and are conversant with local laws, regulations, markets and competition. KPMG has offices across India in Ahmedabad, Bengaluru, Chandigarh, Chennai, Gurugram, Jaipur, Hyderabad, Jaipur, Kochi, Kolkata, Mumbai, Noida, Pune, Vadodara and Vijayawada.

KPMG entities in India offer services to national and international clients in India across sectors. We strive to provide rapid, performance-based, industry-focused and technology-enabled services, which reflect a shared knowledge of global and local industries and our experience of the Indian business environment.

Equal employment opportunity information

KPMG India has a policy of providing equal opportunity for all applicants and employees regardless of their color, caste, religion, age, sex/gender, national origin, citizenship, sexual orientation, gender identity or expression, disability or other legally protected status. KPMG India values diversity and we request you to submit the details below to support us in our endeavor for diversity. Providing the below information is voluntary and refusal to submit such information will not be prejudicial to you.

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

Required Qualifications

• Bachelor of Science or Bachelor of Engineering at a minimum.
• 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

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