9 Best Data Science Roles: A role fit for you
Are you a person who is planning to switch into the fields of data science but confused which role to choose? We bring to you the various roles where you can match your skills and find the best role fit for you. Data Science offers an avalanche of jobs in all the roles listed below. Let’s take a sneak peek into the most trending data science job roles.
1. Data Scientist
Skills Required: Python/R, SQL, Statistics, ETL, EDA, Machine Learning, Deep Learning, Data Visualization, Big Data Processing Frameworks, etc.
Description: Data Scientists gather vast amounts of structured and unstructured data and convert them into valuable insights which helps an organisation to grow and compete. They interpret and analyse data from multiple sources to come up with imaginative solutions to problems which have the potential to drive the growth of organizations. They use analytical techniques like text analytics, machine learning, and deep learning to analyse data. They encourage a data-driven approach to solve complex business problems. They communicate all the productive observations and findings to the company stakeholders via data visualization.
2. Data Analyst
Skills Required: Python/R, Statistics, SQL, SAS, Microsoft Excel, Data Visualization, etc.
Description: Data Analysts are professionals who scrutinise information using data analysis tools. They translate numbers, statistics, figures into plain English for everyone to understand. There’s always an increasing scope for Data Analysts at the workplace, as the meaningful results they pull from the raw data by writing SQL queries help their employers or clients make important decisions by identifying various facts and trends. They extract the relevant insights from the dataset and channelise those ideas through visualizations and reports.
3. ML Engineer
Skills Required: Python/R, Statistics, DSA, ETL, EDA, Machine Learning Algorithms, Apache Spark, Model Deployment etc.
Description: Machine Learning Engineers are made to study and convert Data Science prototypes. They design and develop ML systems and schemes. They find available datasets online for training purposes. They perform Statistical analysis and fine-tune models using test results. They train and retrain ML systems and models as and when necessary. They also work upon existing ML frameworks and libraries to extend and enrich them.
4. MLOps Engineer
Skills Required: Python/R, Statistics, Machine Learning, DevOps, Data Engineering, Docker, Kubernetes, Model Monitoring, etc.
Description: MLOps is used by businesses to run AI successfully. It is a shorthand for machine learning operations. It is modeled on the existing discipline of DevOps, the modern practice of efficiently writing, deploying and running enterprise applications. MLOps needs a powerful AI infrastructure for growth of the companies. MLOps Engineers help and add to the team of Data Scientists, who curate datasets and build AI models that analyze them. This also includes ML Engineers, who run those datasets through the models in disciplined, automated ways.
5. DL Engineer
Skills Required: Python/R, Statistics, ETL, EDA, TensorFlow, Keras, Deep Learning Algorithms, NLP, CV, Software Development, Big Data Analytics, Image Processing, etc.
Description: Deep Learning Engineers tend to be experts in both Machine Learning and Deep Learning. Their primary responsibility is to develop algorithms based on statistical modeling procedures. They need to build and maintain scalable ML/DL solutions in production. They also analyze large and complex datasets to extract insights. They have to train and retrain ML/DL systems as and when necessary.
6. Business Analyst
Skills Required: Python/R, SQL, Story Telling, Microsoft Excel, Data Visualization, etc.
Description: Business Analysts interact with both the business partners and users to understand how data-driven changes to business processes, products/services, and software/hardware can enhance efficiencies and add value to the organization. They work in close collaboration with the IT and Financial reporting teams to design and implement new business models to support the proposed business decisions, establish initiatives/policies, improve productivity and optimize costs. They analyze what’s financially and technologically feasible for the company. They guide enterprises in improving their processes and quality of products/services while ensuring the timely dispatch of client deliverables.
7. Research Analyst
Skills Required: Python/R, Statistics, Research Analysis, Market Research, Online Research, Microsoft Excel, Data Analysis, etc.
Description: Research Analysts closely examine the data and produce meaningful information for their employer. They can be found in both public and private sectors and are necessary for most financial businesses to operate successfully. They use a variety of sources to research and analyze topics, becoming an expert in the field. They collect and analyze quantitative data. They present results in written or oral format. They interview clients or team members to identify information needs and deliverables.
8. NLP Engineer
Skills Required: Python/R, Statistics, Machine Learning, Text Representation Techniques, NLP, Designing Software Architecture, etc.
Description: Natural language processing (NLP) Engineers work to impart machines with the ability to understand natural human languages. They are primarily responsible for designing and developing machines and applications that can learn the patterns of speech of a human language and also translate spoken words into other languages. They develop NLP systems according to requirements. They train the developed model and run evaluation experiments. They perform statistical analysis of results and refine the models. They extend ML libraries and frameworks to apply in NLP tasks. Their goal is to help machines comprehend human languages as naturally as humans do.
9. BI Developer
Skills Required: SQL, Power BI, Tableau, ETL, Microsoft SQL Server, Data Warehouse, Looker, etc.
Description: Business Intelligence (BI) Developers are data experts working with databases and different types of software. They develop and fine tune IT solutions. Their work can include anything from coding to testing to debugging to designing to implementing newly developed tools. They spend a lot of time researching and planning solutions for existing problems in the company. They always mine data and present that in the most understandable way. They create tables and write reports simplifying highly technical language for others to understand in the company.
There are several other job roles like Data Engineers, Database Administrator, Data Architect, Statistician, Data and Analytics Manager, Big Data Engineer, Applications Architect, Enterprise Architect, Infrastructure Architect, Machine Learning Scientist which can be a role fit for you. But these roles generally have overlapped skills required with above mentioned roles and hence are not in much demand. So, if you prepare well working on the required skills for the desired job role then you can be the next one to get the breakthrough.