What Is Enterprise AI?
Data Science Hiring Process At MobiKwik
Less than a week ago, Indian fintech unicorn MobiKwik emerged as the largest digital financial services platform for PPI Wallet transactions by value for April and May, as reported by the RBI, surpassing Paytm, PhonePe, and Amazon Pay, among others.
The IPO-bound company's market share increased from 11% in March 2024 to 20% in April, and 23% in May of this year. MobiKwik has grown rapidly, with over 146 million registered users and 3.8 million merchant partners till now.
Founded almost 15 years ago by husband and wife duo Bipin Preet Singh and Upasana Taku, the company leverages AI and ML to solve complex business problems, improve its service offerings and make them personalised for customers.
Initially a mobile wallet provider, MobiKwik has expanded its offerings to include "buy-now-pay-later" credit, personal loans, merchant cash advances, wealth management, and insurance distribution.
"Our data science approach includes mining unstructured data (NLP), identifying patterns, and deriving meaningful insights to assist businesses in making better decisions," Saurabh Dwivedi, SVP of technology, MobiKwik.
The company is currently expanding its workforce and is looking for skilled data scientists across different domains.
Inside MobiKwik's Data Science OpsAccording to Dwivedi, one of the primary challenges that the Peak XV backed startup's data science team addresses is credit risk assessment, which holds tremendous importance for any fintech company.
It has developed various ML models for products such as MobiKwik ZIP, EMI, and Xtra to enhance the decision-making process and improve the quality of the loan portfolio.
The ZIP underwriting model, for example, is a proprietary ML model that assesses the creditworthiness of users applying for ZIP. It integrates traditional (bureau) and alternative data (device), along with behavioural data from in-house wallets, to forecast the likelihood of users becoming delinquent.
This model, built using data from over a million approved ZIP users, outputs a probability score, categorised into deciles from one (lowest risk) to 10 (highest risk).
Similarly, the Spend Analyser tool categorises unstructured data using regular expressions, pattern identification, keywords, and historical trends. It identifies savings opportunities and explores investment options such as MobiKwik's Xtra 14%, fixed deposits, mutual funds, and lending products.
Additionally, the team employs various data extraction techniques to parse documents and draw insights from diverse structured and unstructured datasets. Along with this, the company is powering its chatbot with LLMs to respond to users' transactional queries in a personalised way.
Tech StackMobiKwik's data science projects leverage a diverse tech stack, including SQL, Python, PySpark, PyTorch, TensorFlow, Keras, LLMs, REST APIs, Docker, and GitLab CI/CD. These tools and frameworks are selected based on the specific requirements of each project to build robust and scalable data science solutions.
Moreover, the team utilises GPT and Transformer Models for NLP tasks, such as transaction pattern analysis, entity extraction, and document analysis.
Interview Process"We evaluate candidates on a comprehensive set of skills, including expertise in machine learning models, deep learning, NLP, conversational AI, and generative AI skills," Dwidedi told AIM, stating that the selection process also considers multimodal communication, creativity, curiosity, and resilience.
Proficiency in prompt engineering, CI/CD, model monitoring, and key performance indicators (KPIs) are essential criteria for candidates.
The interview process at MobiKwik consists of several stages:
Candidates joining MobiKwik's tech team can expect to work on innovative products driven by data science, such as risk modelling, UPI wallet, Lens, ZIP, ZIP EMI, and Xtra.
"Our large data platforms are constructed with extensive data pipelines based on machine learning. We are focused on real-time use cases, using multi-faceted AI capabilities. This serves as a great opportunity for candidates to grow into full-stack ML engineers and data scientists, thereby adding significant value to our team," he added.
On the other hand, the company expects candidates to contribute to business scaling, demonstrate expertise in relevant technologies, and commit to continuous learning.
Work CultureMobiKwik aims to foster a dynamic work culture that includes direct interaction with founders and the senior leadership team, ensuring maximum facetime. "With a grounded leadership ethos valuing attitude over aptitude, we maintain a lean and flat hierarchy," explained Dwivedi.
The company also provides special perks for employees, including group health insurance, personal accident insurance, and special leave allowances. Additionally, employees can take a recharge leave of five consecutive days after completing three years of service, promoting work-life balance and personal rejuvenation.
Check out MobiKwik's careers page here or drop a mail at ta@mobikwik.Com.
Find A Neuro-Linguistic (NLP) Therapist
Is neuro-linguistic programming a type of psychotherapy? No, Neuro-linguistic programming (NLP) is not a form of psychotherapy. Rather, it is a set of principles and techniques intended to help people make healthy changes to their thoughts or behavior. NLP may be used in conjunction with therapy to address mental health issues, but it is not formally regulated. It has also been applied commercially by coaches and other non-therapist practitioners to help individuals achieve career-related goals across a wide range of industries. What is neuro-linguistic programming used for? In the commercial sector, neuro-linguistic programming may help build someone's confidence and increase self-awareness. It may be useful in meeting work-oriented goals, such as boosting productivity or getting a promotion. In a therapeutic setting, NLP is one tool that a therapist may try when treating common mental health conditions, including anxiety, depression, post-traumatic stress disorder (PTSD), fears and phobias, substance misuse, and more.What are some of the core principles of neuro-linguistic programming? Neuro-linguistic programming is intended to help clients better understand their own perspective and become more sensitive to others' points-of-view. NLP recognizes that people form their own internal "maps" based on their sensory experiences. In NLP terms, everyone has a preferred representational system (PRS) that they use to navigate the world around them, which may be primarily visual, auditory, kinesthetic, olfactory, or gustatory. Understanding one's own PRS—and being able to recognize others'—can facilitate communication and connection.
How can I recognize a good neuro-linguistic programming therapist? Look for a licensed mental health professional with additional training in NLP techniques; this can include workshops and mentorship programs. Therapeutic fit is essential to feeling safe and comfortable with an NLP therapist. Potential clients should find out about the therapist's experience helping other people with similar issues. In addition, they should ask the therapist whether they consider the client a good candidate for NLP and why or why not. This helps the client to set reasonable expectations for NLP-assisted therapy.
Speech-based NLP - Worldwide
Data coverage: The data encompasses B2B, B2G, and B2C enterprises. Figures are based on the funding values from different industries for the market.
Modeling approach / Market size:Market sizes are determined through a top-down approach with a bottom-up validation, building on a specific rationale for each market. As a basis for evaluating markets, we use annual financial reports, funding data, and third-party data. In addition, we use relevant key market indicators and data from country-specific associations such as GDP, number of internet users, number of secure internet servers, and internet penetration. This data helps us estimate the market size for each country individually.
Forecasts:In our forecasts, we apply diverse forecasting techniques. The selection of forecasting techniques is based on the behavior of the relevant market. For example, the S-curve function and exponential trend smoothing are well suited to forecast digital products and services due to the non-linear growth of technology adoption. The main drivers are the level of digitalization, the number of secure internet servers, and the revenue of the Public Cloud market.
Additional Notes: The data is modeled using current exchange rates. The impact of the COVID-19 pandemic and the Russian-Ukraine war are considered at a country-specific level. The market is updated twice a year. In some cases, the market is updated on an ad-hoc basis (e.G., when new, relevant data has been released or significant changes within the market have an impact on the projected development). Data from the Statista Consumer Insights Global survey is weighted for representativeness.
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