Assistant Manager

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.

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Forensic-Ftech

KPMG has a dedicated Forensic Technology lab which enables the recovery and use of critical digital evidence to support investigations and litigation. Using various tools (proprietary, open source, vendor tools), we can extract, transform and visualize information from any source in any format, including laptops, mobile phones and other electronic devices.
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‎ The tools can help establish whether evidence has been erased or modified; analyze electronic content and patterns of Internet and e-Mail usage; recover deleted data; and assess and explain metadata within recovered files, even if certain persons sought to destroy it.

1. Strong experience in developing of AI models using multiple programming languages with a strong foundation in a statistical platform such as Python, R, SAS, or MatLab. Proven experience with developing and testing of various ML models, deep learning, Tensorflow and NLP.

2. Experience in validation of existing AI models to check bias & fairness appropriately based on above mentioned risk tiering. High risk models might have high exposure to bias & fairness issues.

3. Analyzing the ML algorithms that could be used to solve a given problem and ranking them by their success probability

4. Exploring and visualizing data to gain an understanding of it, then identifying differences in data distribution that could affect performance when deploying the model in the real world

5. Tune the model by reviewing the parameters for coverage, accuracy, applicability and model bias.

6. Strong expertise in database management software (e.g. SQL Server, Oracle etc.), statistics and machine learning software (e.g. Python, R etc.) and link analysis/data visualization software, big data platforms

7. Work on functional design, process design (including scenario design, flow mapping), prototyping, testing, training, and defining support procedures, working with an advanced engineering team and executive leadership