Nasdaq Women in Technology: Niharika Sharma, Senior Software Engineer, Nasdaq’s Machine Intelligence Lab

Women in Tech: Niharika Sharma

Niharika Sharma is a Senior Software Engineer for Nasdaq’s Machine Intelligence Lab. She designs systems that gather, process and apply machine learning/natural language processing technologies on natural language data, generating valuable insights to support business decisions. Over the past years, she worked on Natural Language Generation (NLG) and Surveillance Automation for Nasdaq Advisory Services. We sat down with Niharika to learn more about how she got her start in computer science and how she approaches challenges in her career.

Can you describe your day-to-day as a senior software engineer at Nasdaq?

My day-to-day work involves collaborating with Data Scientists to solve problems, ideating business possibilities with product teams and working with Data/Software Engineers to transform ideas into solutions.

How did you become involved in the technology industry, and how has technology influenced your role?

My first exposure to Computer Science was a Logo programming class that I took as a junior in high school. After that, I took a couple of coding classes for fun.

When it came to choosing a college major, my high school Mathematics teacher suggested I consider a career in Software Engineering. At first, I thought, “Programming?! That’s too geeky!”. I liked coding, but I never wanted to be that nerd who sits in a cube staring at a computer all day. For college, I chose to study Chemistry at Delhi University, but a few months into the course, I realized technology was where I belonged, and I eventually pivoted to Engineering.

A decade later, I admit that it was the best decision I ever made. I found the concepts and problem solving so engaging that after obtaining my degree, I took a leap of faith and moved to the U.S. to pursue a Masters in Computer Science from Northeastern University. In the final semester, I

Robotic Interviews, Machine Learning And the Future Of Workforce Recruitment

These would affect all aspects of HR functions such as the way HR professionals on-board and hire people, and the way they train them

Grow Your Business, Not Your Inbox

Stay informed and join our daily newsletter now!


4 min read

Opinions expressed by Entrepreneur contributors are their own.


You’re reading Entrepreneur India, an international franchise of Entrepreneur Media.

Artificial intelligence (AI) is changing all aspects of our lives and that too at a rapid pace. This includes our professional lives, too. Experts expect that in the days ahead, AI would become a greater part of our careers as all companies are moving ahead with adopting such technology. They are using more machines that use AI technology that would affect our daily professional activities. Soon enough, we would see machine learning and deep learning in HR too. It would affect all aspects of HR (human resources) such as the way HR professionals on-board and hire people, and the way they train them.

Impact on onboarding and recruitment

These days, companies are using robotics in HR to make sure they have found the right people for particular job profiles. This means that even before you have stepped into your new office, your company already knows that you are the best person for the job thanks to such technology. They are using AI to pre-screen candidates before they invite the best candidates for interviews. This especially applies to large companies that offer thousands of new jobs each year and where millions of applicants go looking for jobs.       

Impact on training on the job

Companies are also using machine learning and deep learning in HR to help provide on-the-job training to employees. Just because you have landed a job and settled in it, it does not mean that you know

Introducing IFRS 17 Software With Machine Learning

Press release content from Newswire. The AP news staff was not involved in its creation.

LONDON – October 8, 2020 – ( Newswire.com )

Data is universally indicated as the key challenge in IFRS 17 implementations. “We designed our IFRS 17 solution from start to finish with data quality in mind. 3Blocks has differentiated itself in this regard by adding machine learning to the arsenal of data checks and validation. We are the first on the market to do that,” says Pawel Wozniak, CEO of 3Blocks.

3Blocks has been known for providing IFRS 17 and Solvency 2 reporting advisory services, but now it has launched its own IFRS 17 software. They have been working on IFRS 17 projects for insurance companies and other IT firms since 2016. Their solution has been built on the back of these experiences, taking into account lessons learned from them. 3Blocks PAA is intended for small and medium-sized non-life insurance companies that do not have the time and budget to implement expensive alternatives offered by competitors.

Algorithms designed by 3Blocks learn what patterns are exhibited by claims, expenses, premiums, and other reporting data. Then, users can check if the actual amounts fit the pattern. Significant variances may indicate data issues that require fixing.

Machine learning is not the only trick that 3Blocks has up its sleeve. Most IFRS 17 software solutions available on the market require data on a defined level of granularity. For example, they require that all data should be by group or by policy. 3Blocks software gives full flexibility in this respect, as data and calculations can be arranged by policy, group, or portfolio, with a possibility to mix levels. Secondly, the solution supports three different accounting rules sets, so whatever accounting rules are used by an insurance

How machine learning is different from conventional programming language?

Machine learning and conventional programming language are two different approaches to computer programming languages that yields different outcomes or expectations.

By definition, Machine Learning is a field of software engineering that enables PCs to learn without being unequivocally modified. AI shows PCs the capacity to take care of issues and perform complex errands all alone. Much of the time, issues unraveled utilizing AI depend on the PC’s learning experience for which they wouldn’t have been settled by ordinary programming dialects. Such issues can be face acknowledgment, driving, and ailments’ conclusion. With regular programming language, then again, the conduct of the PC is coded by first making a reasonable calculation that keeps predesigned sets of rules.

In other words, machine learning depends on a rather different form of augmented analytics where input and output data are fed into algorithms. The algorithms then create the program. On the contrary, conventional programming languages involve manually creating programs by providing input data. The computer then generates an output based on programming logic. For instance, you can easily predict consumer behavior through trained machine learning algorithms.

Another significant contrast between machine learning and conventional programming language is the precision of expectations. Conventional programming language relies upon calculations inside an assortment of info boundaries. Machine Learning then again gathers information dependent on past occasions (verifiable information) which construct a model that is equipped for adjusting freely to new arrangements of information to create solid and repeatable outcomes. This sort of self-learning models can’t be worked with customary programming dialects.

However, with machine learning, there are no restrictions on the number of data sets and models that can be generated since the built models are capable of learning independently. As long as you have enough processor power and memory, you can use as many input parameters and

Machine Safeguarding Solutions Market with COVID-19 Recovery Analysis 2020-2024|Growth of End-user Industries to Boost Market Growth

The global machine safeguarding solutions market size is poised to grow by USD 774.41 million during 2020-2024, progressing at a CAGR of almost 4% throughout the forecast period, according to the latest report by Technavio. The report offers an up-to-date analysis regarding the current market scenario, latest trends and drivers, and the overall market environment. The report also provides the market impact and new opportunities created due to the COVID-19 pandemic. Download a Free Sample of REPORT with COVID-19 Crisis and Recovery Analysis.

This press release features multimedia. View the full release here: https://www.businesswire.com/news/home/20201007005678/en/

Technavio has announced its latest market research report titled Global Machine Safeguarding Solutions Market 2020-2024 (Graphic: Business Wire)

The machine safeguarding solutions market is driven by the growth of end-users. Several machining operations that are carried out in the automotive and industrial machine manufacturing industry involve bending, boring, grinding, and milling. Manufacturers use transmission systems such as flywheels, belts, pulleys, motors, and gears to operate auxiliary systems such as compressors and pumps, machine tools, and packaging lines. The increased use of these tools and systems can pose a significant safety hazard to the operators and stimulate the need for adequate machine safeguarding solutions to eliminate or reduce safety risks.

Register for a free trial today and gain instant access to 17,000+ market research reports.

Technavio’s SUBSCRIPTION platform

Report Highlights:

  • The major machine safeguarding solutions market growth came from the switches segment and the segment is expected to witness the fastest growth during the next five years.

  • Europe was the largest machine safeguarding solutions market in 2019, and the region will offer several growth opportunities to market vendors during the forecast period. This is attributed to the growing adherence to strict compliances with workplace safety regulations.

  • The global machine safeguarding solutions market is fragmented. ABB Ltd., Eaton