A data science job at the company you love? Yes you can.

Graduates face hurdles when choosing the right company to launch their careers. The data science profession is no exception.

As a teacher and instructor, I come across students who have no idea of their next career move and often make bad decisions.

Taking a course in data science is no easy road and requires hard work. After you graduate, another uphill task awaits you –Getting hired at your favorite company.

There are many companies out there with data science roles¹ and settling on the best requires research and networking as you start. Students and data scientists have the notion that big corporations offer better options and benefits not available elsewhere.

This is not necessarily true.

Even small companies and start-ups can offer the best platform for your #datascience career. Nevertheless, this does not mean looking for scrappy start-ups that will not guarantee you a good career foundation.

Do not underestimate small and mid-sized companies as these grow eventually and become big companies. Even start-ups offer the best starting point for your data science career.

The bottom line: You need an open mindset that will get you the best career option under the current conditions.

Let us review some important considerations to get you a data scientist job at the company of your choice:

1. Conduct Interviews with Company Employees

I remember when I completed my undergraduate degree; I set my sight on the finance sector where I began working in banks and insurance companies with data science positions².

Before my first gig in the finance industry, I remember interacting with employees by asking them about their working schedules, policies and other questions which helped me to understand the situation.

You should do the same.

Discuss with employees you know at the company of your interest. This will not only improve your understanding of the job market but also prepare you for the road ahead³.

Some graduates rush into data science jobs, fail to do some scouting and later regret their choices. For example, some companies shift their policies after hiring #datascientists and this could take you in the wrong direction.

What is important in any career than networking?

Yes, this helped me understand current issues in data science and job roles in different companies as I started out. I recall attending networking events and talking to experts about their experiences. For example, one colleague mentored me on different skills in the finance industry to increase my chances as a data scientist.

Corporate organizations organize hiring sessions⁴ where representatives host panels for recruiting candidates. Data scientists should take advantage of these opportunities to understand expectations prior to #hiring. You need to attend these sessions, interact with fellow job seekers and those hiring to get an accurate perspective of the situation.

Corporate organizations organize hiring sessions where representatives host panels for recruiting candidates

2. Track your Job Search

The first step to getting a good company as a data scientist is tracking your job search.

What does job search tracking mean?

This is compiling the target companies of your interest and evaluating their responses. Consistency matters as data science candidates assume that companies will hire them automatically. You need to show interest and it does not cost anything to keep checking responses and hiring news.

Many great websites can help you track your job search.

Indeed.com is one of them and allows users to save job searches in order to keep track of job titles employers are looking for. Data science candidates⁵ should track job searches and use them to find make the best choices.

Another website is Zip recruiter and consists of features that improve employee job search. Besides tracking your job, websites including Simply Hired and Career Builder have email alerts sent directly to your phone.

Why not use these websites to get a view of the current #jobmarket?

3. Consider Multiple Cities

Here is one interesting thing about me.

I grew up in South Florida, and spent most of my years here until I graduated from college. During my job hunt, the idea of relocation did not cross my mind but with time, I became open to this idea.

Many graduates and job seekers⁶ prefer their cities of choice but the reality is that when a good job comes calling, you cannot reject the opportunity in favor of living in your city. I made the hard choice of moving to New York. This is where I built my career since then, and I have never looked back.

Are ready to make hard choices?

Cities such as San Francisco, New York, Boston and Chicago have great data science companies that you can consider. Regardless of your #location, you cannot get everything you want home and the same is true for data science companies.

Some candidates are stuck in their data science careers because of not moving to areas with good companies and affects their career growth. This can be a costly mistake. The best company could be outside your city and when the time for moving comes, you need to take this step.

The choice is yours

4. Stop Undermining Small Companies

A recent survey conducted by ZDnet⁷ found that data scientists prefer working in big corporations even after graduation. The poll further found that these candidates disregard small companies in favor of landing a job in a big company.

Let us come back to the real world for a moment here:

You need to take those baby steps as a data scientist. Personally, I started small even sometimes doing volunteer work as this offered me enough experience to become better. Unfortunately, data scientists graduating have unrealistic expectations that often hold them back from making progress.

Many small companies offer good pay packages that most data scientists cannot imagine. You need a positive mindset about small companies and understand that they are growing hence the need for patience.

Besides that, these companies offer #trainingprograms⁸ for data science that even big corporations have no time to focus on. Data scientists need exposure about the industry and starting small is the best option. By working in small companies, you will understand current industry trends as a data scientist and get an accurate view of your career choices.

5. Learning and Upskilling

Some core skills in data science⁹ such as Python, SQL, statistics and machine learning matter for every data scientist.

Are you learning and adjusting your skills to fit in the market. If not, then continue learning¹⁰ to remain relevant as you await that job in your target company.

The industry is changing and what better option is there than upskilling?

6. Skip the Cover Letter

My last advice to data scientists looking for a job in their dream company is not to put too much focus on their cover letter. This does not mean a cover letter is not important. It remains vital in your quest for a data science role at the company of your interest. Highlight your core skills and sell yourself by using them.

Skills are your selling point

Relevant Titles in Data Science Profession

Let us look at some relevant titles used in data science¹¹. This is important to help you navigate towards your preferred company as a data scientist.

1. Data Engineer

This role requires candidates with knowledge on data access and storage infrastructure alongside development of software. Programming is another core area of data engineers as they create data pipelines for companies. As a data engineer, you will need an understanding of organization data infrastructure and deriving models for strategic management.

2. Machine Learning Engineer

A machine-learning engineer works in the same role as a data scientist but only that they execute software after analysis by data scientists. Data science programming is one area machine learning engineers need training to become better.

3. Quantitative Analyst

This job title is more about predictive risks such as those used in the finance sector and application of statistical evaluations. Markets need prediction for decision-making and a quantitative analyst best performs by using statistics. The role also requires deployment of machine learning models in market analysis and risk evaluation.

4. Data Warehouse Architect

Are you into data storage? Then this is our job! A data warehouse architect oversees management of data for an organization. Acquisition of SQL is critical for a data warehouse architect in addition to a wide range of skills including programming.

5. Business Intelligence Analyst

Business trends are important in this digital age and every organization needs professionals skilled in this area. This is where a business intelligence analyst comes in to position the company via market analysis.

6. Statistician

To become a statistician, you need understanding of mathematics. Additionally, learning probability is an added advantage and combined with a mathematical background, you will be crushing it.

7. Business and Systems Analyst

A business analyst assists companies to find optimal solutions to hard questions about their operations. You need an understanding of data as a business analyst. Given the difference between a business and systems analyst, most companies do not necessarily require business analysts to have all skills of a data scientist.

On the other hand, a systems analyst should explore challenges facing an organization and develop solutions that align with the mission. Statistical analysis is essential for a business analyst because of interpreting data about the business, markets or even trends.

8. Marketing and Operations Analyst

A marketing analyst uses data about the market ¹² for decision-making and do this by evaluating metrics in their line of business. The marketing analyst needs a background knowledge on analytics as these go together with data analysis.

An operations analyst focuses on the current issues within the realms of a business. Unlike most analyst positions that require a broad overview of company environments, the operations analyst concentrates within the organization and not outside.

Cheers to getting your Data Science Job

The road to working at the company you love is not easy. Big companies have slim chances of landing a job because of competition. As mention earlier, why not start small and use this as a platform to grow. Similar to data science, many companies need candidates with better market exposure and working in different companies as you target your main company is a good start.

Reaching at the top has never been a straight path in any field and data science students need to embrace this reality. Who knows, as you start small, your skills will become better and the company you love will come calling and hire you. All the best!

Works Cited

¹Data science roles, ²Data science hiring standards, ³Preparing for Data science career, ⁴Corporate hiring in Data science, ⁵Data science candidates, ⁶Graduate job seekers, ⁷ZDnet Survey, ⁸Training programs, ⁹Core skill areas, ¹⁰Keep Learning, ¹¹Relevant Titles in Data Science, ¹²Market Data