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How To Transition To A Data Science Role After Being Laid Off As An Engineer


How To Transition To A Data Science Role After Being Laid Off As An Engineer


One of the most popular choices to hire in the place of engineering role is that of an ML engineer or a data scientist. If you are a software engineer who has been downsized, the best option is to upskill and get into being a data scientist, engineer or machine learning developer.



Identifying What The Job Needs

Self-assessment: Before making the switch, it is important to identify the strengths and weaknesses. Different positions, such as data analysts, data scientists, data engineers and more, all have different roles and responsibilities. One should identify their core competencies and pick a job that is suitable.

For example, data scientists and analysts require an in-depth knowledge of statistics, math and visualisation. In addition to this, they also require good communication skills. Data analysts, in particular, are also required to have business knowledge for actionable insights.

KRA Analysis: Data engineers, on the other hand, are required to fulfil a different set of business roles. This generally requires skills such as programming, database management and administration, data storage and system implementation.

Tool Kit: While the hard skill requirements, such as knowledge of Python/R and SQL are similar among all positions, the soft skill requirements and responsibilities are different. This makes it the first step towards switching and identifying the best position for you.

Discovering The Required Skill Set

The next step is seeing whether one has the required skill set to be a good fit for the job. Any position in the data science field is highly technical, and an integral understanding and interest must be present to ensure that job satisfaction is lost.

Right Job For The Right Interest: A love for data is required for any position, especially with respect to finding patterns in data and knowing how to wrangle it. Data cleaning, pre-processing and handling are some of the most important and time-consuming tasks in data science, which is to be kept in mind before deciding to move into the field.

A strong base in programming is required, as coding is required at almost every step in the way. From managing cloud operations to creating a pipeline to deploying a model, coding skills are required in every step. Moreover, industry best practices and code preservation for uptime are also an important skill to have.

Responsibilities and Roles: The main responsibility of a data engineer is to ensure that data flows smoothly in the workflow or pipeline. This is to be done in such a way as to ensure teammates have access to the data at a low cost, requiring data engineers to have a big picture view of operations.

Data scientists, on the other hand, mainly focus on deriving insights from the data using models, algorithms and other methods. This is to be done keeping in mind the operations of the company itself and has to be visualised and presented in a consumable way for non-technical participants.

In case of switching, these requirements must be considered to ensure a good job fit and reliability.

Learning The Technology And Architecture

If you are a new entrant into the data science sphere, the first thing to do is to look at the tools used by those already in the space. Owing to the vast variety of solutions and competing products in the market, data scientists are spoilt for choice.

See Also

Industry Knowledge To Pick The Best Tools: This also means that it is important to know the most useful ones, so as to pick the right tool for the right job. Every data workflow and pipeline is different, and a data engineer is required to know the best tools to wrangle data from different kinds of datasets.

This includes big data frameworks such as Hadoop, Spark, Kafka, Hive and model frameworks such as TensorFlow, PyTorch and more. In addition to this, in-depth knowledge of SQL is required, as data engineers find most of their responsibilities falling into the database administrator domain.

Soft Skills And Mathematics: Data scientists, on the other hand, are required to keep up to date with the rapidly evolving landscape of AI technology. This is due to the fact that researchers create better ways to solve existing problems using cutting-edge models and algorithms.

In addition, there is also a lot of statistical and mathematical knowledge required to function as data scientists. Moreover, languages such as Python, SQL and R are integral to the data scientists' workflows.

In Conclusion

Before making the switch to another role or field, especially data science, the above factors must be considered. The skill sets of software engineers, such as programming and analytics knowledge, transfers well to the data science field, making it a good fit for engineers looking to switch.



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