Machine Learning is one of the hottest career choices today. AI will create 2.3 million Machine Learning jobs by 2020, according to a recent report from Gartner. LinkedIn’s 2017 U.S. Emerging Jobs Report shows there are 9.8 times more Machine Learning Engineers working today than five years ago with 1,829 open positions listed on their site as of early 2018. Both Data Science and Machine Learning are generating more jobs than candidates right now, making these two areas the fastest growing tech employment areas today.
These jobs in demand include research and development of algorithms that are used in adaptive systems across Amazon. ML scientists build methods for predicting product suggestions and product demand and explore Big Data to automatically extract patterns. Companies recruit for positions like Machine Learning Engineer, Machine Learning Analyst, NLP Data Scientist, Data Sciences Lead, and Machine Learning Scientist.
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Whether you are looking for Machine Learning Jobs in Bangalore or Machine Learning Jobs in Pune, or Machine Learning jobs in Hyderabad, Machine Learning Jobs Salary in India is reasonable good. ML jobs for freshers may vary between ₹ 699,807- 891,326. With a sound knowledge of data analysis and algorithms and a few years of experience, you may expect a salary of ₹ 1,759,777 monthly or ₹ 9, 00,000 per annum. From smartphones to chatbots, demand for these jobs is at an all-time high, so it is just the right time to get in on the ground floor of a growing industry.
Essential Machine Learning Skills Sets to become a Machine Learning professional
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Computer Science Fundamentals and Programming:
Having a Computer Science background is very important to have a rewarding career in Machine Learning. Engineers looking for Machine Learning Jobs in India should have in-depth knowledge of data structures (stacks, queues, multi-dimensional arrays, trees, graphs, etc.), algorithms (searching, sorting, optimization, dynamic programming, etc.), computability and complexity (P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc.), and computer architecture (memory, cache, bandwidth, deadlocks, distributed processing, etc.). One must be able to apply, implement, adapt or address them when programming.
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Machine Learning Algorithms and Libraries:
Candidates looking forward to Machine Learning jobs in India should be well acquainted with standard implementations of Machine Learning algorithms, most of which are widely available through libraries/packages/APIs .one should also be aware of the relative advantages and disadvantages of different approaches.
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Probability and Statistics:
If you are looking for a career in Machine Learning, you should possess strong knowledge of formal characterization of probability and techniques derived from it (Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc.). Also, knowledge of analysis methods (ANOVA, hypothesis testing) is necessary for building and validating models from observed data.
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Software Engineering and System Design:
Having a strong base in software engineering and system design is required for a promising career in Machine Learning and data science. It also adds on to your Machine Learning skills. You should be able to build appropriate interfaces for your component. Having a good knowledge of Software engineering best practices (including requirements analysis, system design, modularity, version control, testing, documentation, etc.) are invaluable for productivity, collaboration, quality, and maintainability.
Machine Learning Job Roles
ML jobs in India and abroad include Machine Learning Engineer, Data Architect, Data Scientist, Data Mining Specialists, Cloud Architects, and Cyber Security Analysts, and many more. Let us take a sneak peek into some of the ML job roles in demand. Machine Learning Jobs for freshers may include the job of a data analyst or data scientist.
Machine Learning Engineer:
A Machine Learning Engineer creates amazing algorithms to help decipher meaningful patterns from humongous amounts of data. ML Engineers should focus on Python, Java, Scala, C++, and JavaScript. He must be capable of building highly-scalable distributed systems and work in teams that focus on personalization. The engineers in this field have to design and implement Machine Learning applications/algorithms such as clustering, anomaly detection, classification, or prediction to address business challenges.
Data Engineer/Data Architect:
Data Engineers are responsible for the organization’s big data ecosystem. With a strong foundation in programming, they must be familiar with Hadoop, MapReduce, Hive, MySQL, Cassandra, MongoDB, NoSQL, SQL, Data streaming, and programming. In addition, they must also be proficient in R, Python, Ruby, C++, Perl, Java, SAS, SPSS, and Matlab. Data infrastructure engineers develop, construct, test, and maintain highly scalable data management systems. Data Engineers also develop custom analytics applications and software components. Data engineers collect and store data, do real-time or batch processing, and serve it for analysis to data scientists via an API.
Data Scientist:
One of the most in-demand professionals today, Data Scientists are experts in R, SAS, Python, SQL, MatLab, Hive, Pig, and Spark. They are proficient in Big Data technologies and analytical tools. They use coding to sift through large amounts of unstructured data to derive insights and help design future strategies. Data scientists clean, manage and structure big data from disparate sources.
Data Analyst:
Most organizations expect Data Analysts to be familiar with data retrieval and storing systems, data visualization and data warehousing using ETL tools, Hadoop-based analytics, and business intelligence concepts. These persistent and passionate data miners usually have a strong background in math, statistics, Machine Learning, and programming. Core responsibilities of a Data Analyst include designing and deploying algorithms, culling information and recognizing risk, extrapolating data using advanced computer modeling, triaging code problems, and pruning data.
Machine Learning Jobs Salary
The Indian Data Analytics Industry, with its current worth of $2 billion is expected to witness a whopping eight-fold growth and to be worth $16 billion by 2025, according to NASSCOM. Furthermore, the study indicates that the median salary of analytics professionals is growing year on year. The average salary of Data Science professionals across all skill sets and experience levels was ₹ 12.7L in 2017, an 8% increase since 2016, on a much larger base of professionals. The salary for Machine Learning Jobs for Freshers may start at ₹ 8L and may go up to ₹10-15 L.
According to the latest industry estimates, an AI professional with an experience of 2-4 years can earn annually ₹15-₹20 lakh ($20.6-27.5K). Experienced professionals with 4-8 years can expect up to ₹20-₹50 lakh ($20.6-68.8K). The salaries may vary from company to company. Top-notch companies like Amazon India, Google, and Flipkart are ready something between 8-12 L per annum. Most AI and Machine Learning are concentrated in IT, FinTech and e-commerce, as they rely on analytics, business intelligence and cloud for industrial automation.
Machine Learning Jobs Salary in India
According to Ravi Kaklasaria, CEO& Co-Founder, SpringPeople, a corporate training and certification provider, India has over 50,000 open data analytics jobs on the market and expects the numbers to grow up to 100,000 in 2018. BN Thammaiah, Managing Director, Kelly Services India expects a 60% increase in demand for AI and Machine Learning specialists in 2018. The increase in the demand for AI and ML specialists will be from 20,000 to 32,000 in 2018 even as the IT sector will continue to hire for newer skills, he added further.
As per Analytics India Magazine ‘Salary Study – 2018 that explores a range of current and emerging compensation trends in the Analytics & Data Science space across India. This data in India observes that nearly 40% analytics professionals earn an annual salary upwards of ₹10L. In 2018, 37.6% of Analytics professionals in India received a salary of fewer than 6 Lakhs, compared to 39% in 2017 (39%) and 42% in 2016.
Mid-level analytics professionals are now progressing towards a higher salary bracket of ₹ 15 to 25L by having proper machine learning requirements. While the number of Analytics professionals commanding salaries less than ₹ 10 Lakhs has gone down. Whereas, the number of Analytics professionals earning more than ₹ 15L has increased from 17% in 2016 to 21% in 2017 to 22.3% in 2018.
Mumbai leads in terms of the highest compensation packages for Machine Learning jobs in India, at almost 13.3L per annum. Machine Learning jobs in Bangalore follow close quarters, with 12.5L and NCR at 11.8L. Data professionals looking for Machine Learning jobs in Pune can expect something around ₹ 10.6L. Salary packages for Machine Learning jobs in Hyderabad are comparatively, at ₹ 10.2L per annum.
The Future of Machine Learning
Future of Machine Learning looks promising. There is an urgent need for professionals who are trained in Deep Learning and AI jobs and match machine learning requirements. If you want to be one of those professionals, prepare yourself by getting certified and industry-ready because the sooner you get your training started, the sooner you will be working in this exciting and rapidly changing field.
You might be a programmer, a mathematics graduate, or simply a bachelor of Computer Applications. Students with a master’s degree in Economics or Social Science can also be an ML professional. Take up a Data Science or Data Analytics course, to learn Machine Learning & Data Science skills and not only prepare you for the Machine Learning job, but also gives a proper overview of all the machine learning requirements.
You may also enroll in Machine Learning Course for more lucrative career options in Data Science. Industry-relevant curriculums, pragmatic market-ready approach, hands-on Capstone Project are some of the best reasons for choosing Digital Vidya.
For a rewarding career in Machine Learning, one must stay up to date with any up and coming changes in the machine learning requirements. This also means staying abreast of the latest developments for tools (changelog, conferences, etc.), theory and algorithms (research papers, blogs, conference videos, etc.).
Online communities are great places to know about these changes. Also, read a lot: read articles on Google Map-Reduce, Google File System, Google Big Table, and The Unreasonable Effectiveness of Data. You will get plenty of free Machine Learning books online. Practice problems, coding competitions, and hackathons are a great way to hone your machine learning skills.
