Machine Learning – Career Prospects

To state that Machine learning is a growing field would be an understatement.
Here’s some perspective:
According to a report from the leading job site Indeed, machine learning engineers are in high demand with the opportunity of a career boasting an average salary of $146,085 at a growth rate of 344 per cent from last year.
All industries now have a multitude of applications in machine learning, which is the primary reason why there is a high demand for jobs in that field. If you’ve been waiting, now is the right time to consider pursuing a career in ML.

Why Pursue a Career in Machine Learning in 2019?

Machine learning is the fuel we need to power robots, alongside AI.
With ML, we can power programs that can be easily updated and modified to adapt to new environments and tasks- to get things done quickly and efficiently.
Here are a few reasons for you to pursue a career in ML:
– ML is a skill of the future – Despite the exponential growth in machine learning, the field faces skill shortage. If you can meet the demands of large companies by gaining expertise in ML, you will have a secure career in a technology which is on the rise.
– Work on real challenges – Businesses in this digital age face a lot of issues that ML promises to solve. As an ML engineer, you will work on real-life challenges and develop solutions that have a deep impact on how businesses and people thrive. Needless to say, a job that allows you to work and solve real-world struggles gives high satisfaction.
– Learn and grow – Since ML is on the boom, by entering into the field early on, you can witness trends firsthand and keep on increasing your relevance in the marketplace, thus augmenting your value to your employer.
– An exponential career graph – All said and done, Machine learning is still in its nascent stage. And as the technology matures and advances, you will have experience and expertise to follow an upward career graph and approach your ideal employers.
– Build a lucrative career– The average salary of an ML engineer is one of the top reasons why ML seems a lucrative career to a lot of us. Since the industry is on the rise, this figure can be expected to grow further as the years pass by.
– Side-step into data science – Machine learning skills help you expand avenues in your career. ML skills can endow you with two hats- the other of a data scientist. Become a hot resource by gaining expertise in both fields simultaneously and embark on an exciting journey filled with challenges, opportunities, and knowledge.
Machine learning is happening right now. So, you want to have an early bird advantage of toying with solutions and technologies that support it. This way, when the time comes, you will find your skills in much higher demand and will be able to secure a career path that’s always on the rise.

What Does the Career Path in Machine Learning Look Like?

A machine learning career path usually begins as a Machine Learning engineer. Machine learning engineers develop applications and solutions that automate common tasks previously handled by humans. Most of these are repetitive tasks based on condition and action pairs- which machines can perform without errors, efficiently.
When you earn a promotion as an ML engineer, you step onto being an ML Architect. People in this role develop and design prototypes for applications that need developing.
A few other roles available in the field are ML data scientist, ML software engineer, senior architect, and so on.
A software engineer with enough knowledge of Python and the core ML libraries can switch careers into ML.
Here are a few other tech areas that help if known by an ML professional:
– Probability and Statistics – A lot of ML algorithms have their base in Bayes rule, Markov models, and other probability processes. There’s also statistics- mean, median, deviation, Poisson distribution, and so on.
– System Design – ML solutions are rarely standalone products. Mostly, these are part of an integrated tech ecosystem. Therefore, it helps ML professionals to have sound knowledge of software design.
– ML Algorithms and Libraries – Having knowledge of models such as Linear Regression, Bagging, Boosting, and Genetic algorithms prove useful for ML professionals.
– Data Modeling – As an ML practitioner, you should be capable of estimating the structure of a dataset to find patterns, cluster, and correlations. Data models also need continuous evaluation to ensure they are on point. Additionally, you should even know how to test the data that is being evaluated for accuracy and completeness.
– Programming Languages – Python is a crucial programming language for anyone looking to build a career in ML. Apache Spark is another technology, followed by SAS.
This is not a comprehensive list which can be undertaken once and then is done with. Aspirants need to be on their toes, always proactive in upgrading their skills and knowledge if they want to pursue an upward career graph.
Pursuing a career in ML can help you be an active part of the digital revolution we talk about in sectors ranging from healthcare to retail, logistics, manufacturing, and so on. Having ML skills makes you a hot resource in any sector, which leaves a lot of open avenues for you to choose from. This way, you are in total control of your career as an ML professional. Take a look at our Machine Learning program if you’re further interested in building a career in Machine Learning.

Machine Learning Career Prospects

Machine learning has emerged as the fastest-growing domain, generating more jobs than candidates available. Whether it is the domestic or international market, machine learning professionals are getting lucrative offers from various industries. 
From developing and implementing algorithms for top e-commerce platforms to predicting product suggestions, Machine learning scientists are building exceptional futuristic business solutions. Let’s look into some of the ML profiles that are in demand in the market right now:
– Machine Learning Engineer: ML engineers mostly work with algorithms that focus on deriving patterns out of a copious amount of data. Programming languages like Python, Java, C++, Scala, and Javascript are few of the most popular tools used by ML engineers. A Machine Learning engineer is well versed not just in programming but also in Data Modelling, System Design, Probability and Statistics etc. ML algorithms involving classification, anomaly detection, clustering, and more are equally important for ML engineers. They mostly focus on building high-scalable distributed systems. As with any other AI professional, ML engineers too need to have a sound knowledge of the industry they are working in. It’s only when machine learning engineers combine their technical skills with business acumen that they are able to build machine learning models successfully towards business solutions. 
Skills:

• Deep learning frameworks such as TensorFlow or Keras
• Knowledge of machine learning language libraries like pandas or scikit-learn
• Knowledge of Hadoop or another distributed computing systems
• Data visualisation and modelling
• Proficiency in OpenCV
• Knowledge of ML model compatible hardwares
• Familiarity with open-source operating systems like Linux 

Responsibilities:

• Understand business objectives and develop machine learning models to achieve and monitor their performance
• Manage resources like hardware, data and systems for a smooth functioning
• Analyse ML algorithms to solve problems and rank them by their success probability
• Explore and visualise data to understand and identify leverage points and deploy data models
• Data cleaning and hygiene
• Train and retrain systems as per requirement

– NLP Scientist: NLP or Natural language processing enables machines to decipher human languages and learn its logics and structures. NLP is increasingly influencing the ways of human communication. NLP scientists use syntactic and semantic analysis to help machines read and understand any form of written or oral language communication. Their primary focus ranges from lemmatization, Parsing, stemming, parts-of-speech tagging, morphological segmentation, word and sentence segmentation.
NLP scientists create machine learning models that can translate one language into another, identify syntactic and semantic problems in any kind of digital content and aid auto-communication channels (chatbots etc). 

Skills:

• Learn, build, deploy, run and debug code
• Expert knowledge of Applied ML, Neural Networks and other Deep learning models
• Programming language skills like Python, Java, SQL, NoSQL databases
• Fundamental knowledge of AI tools like TensorFlow, Spacy, Google Cloud ML
• Ability to productise research efforts and channel all efforts towards real product development.

Responsibilities:

• Develop algorithms for deep learning NLP models
• Generate and implement new ideas for applied AI 
• Create AI solutions for automated dialogue and natural texting for product communication
• Build high-performing, real time, end to end recommendation/request systems powered by NLP and other ML techniques
• Build and train NLP platforms through gathered data

Data Science Lead:
The role of lead data scientists involve being hands on with data analytics and to some extent people management. Data science leads manage data science teams, plan projects and build analytical models. In addition to providing business solutions they are also responsible for statistical analysis to drive performance metrics. 
Aligning product data with business goals is one of the primary requirements of the role. 

Skills:

• Highly skilled in statistical and modelling packages such as SAS, Statistica, Matlab, R, Visualisation and other advanced analysis tools. 
• Expert data management and programming skills like SQL, PL-SQL, and Python 
• Familiarity with motion tracking and time-series analyses.
• Strong interpersonal and communication skills

Responsibilities:

• Manage a team of data scientists, ML engineers and big data specialists
• Lead data mining and collection processes
• Analyse and solve data problems
• Generate and maintain data quality and integrity
• Conceive, plan and execute data projects
• Align data projects with business goals
• Build analytical systems with predictive models

Machine Learning Scientist:

ML scientists are responsible for information extraction, graph models, AI architectures, and other AIML projects. They use shallow and deep learning natural language processing tools, statistical modelling and neural networks to build the cognitive and social processes involved in language-based interaction and decision making. 

Skills:

• Ability to understand and develop cognitive structures
• Hands-on experience of structured, unstructured and aggregate data for developing pattern analysis algorithms
• Programming language skills in C++, Java and Python
• Ability of manage productionised research projects
• Proficiency of PyTorch, Tensorflow, or TPU accelerated framework
• Knowledge of model scalability
• Quantitative background in Linear Algebra, Statistics, and Probability
• Knowledge of distributed computational frameworks

Responsibilities:

• Write, evaluate and maintain production-quality code
• Publish research findings in leading academic venues 
• Collaborate with researchers in AI groups on live data

Human-centred Machine Learning Designer:
The demand for human-centred machine learning is on the rise now, as businesses look for personalised solutions. Human centred ML designers work on various systems that recognise patterns from information processing. They help machines to learn the preferences of individual humans and use that information to build smart recommendation systems. Google, Amazon, Netflix and other top companies leverage on human-centred ML designs to provide users with smart customer experiences and as you can imagine the demand for skilled professionals is quite high in this domain.