AMA with Khaya Mkhwanaz and PyLadies Berlin

Looking to make your next career move in Berlin but not sure where to begin? From the 17th to the 21st of July, Khaya Mkhwanaz hosted a Slack AMA in collaboration with PyLadies Berlin, where she answered questions about the Berlin job market and getting a first role in tech. Here are the top questions from the AMA to help you make your next move with confidence.

OfferZen_AMA-with-Khaya-Mkhwanaz-and-PyLadies-Berlin_inner-article

If you have little to no prior experience, what is the best way to stand out when applying?

I believe the best way to stand out is to tailor your resume for the role you are applying for E.ven if your prior experience is not in the same industry, find ways to make your roles and responsibilities match the new role you are applying for.

For example, a PhD student looking to transition into the industry as a data analyst may list things like their expertize in conducting complex research, collecting and analyzing large datasets and deriving meaningful insights from data. They could also highlight any statistical software proficiency they’ve used during their studies.

Is it wise to apply for mid-level jobs when no junior positions are available?

I think that depends on your prior experience and your confidence with the tech stack the company is using. Could you deliver to a mid-level standard in a tech assessment? If you got the role, would you be able to operate independently, with the support a mid-level developer would receive, as opposed to a junior level?

If there are no roles available, most companies have an email address or portal where you can submit your details and resume, just in case a more suitable position becomes available in the future. Alternatively, follow them on LinkedIn and keep an eye on their job openings!

If I want to transition to a different role (i.e., from backend to data engineer), what tips would you give me? Are personal projects enough to land a new role in this new field? Should I expect a drop in seniority and pay? How do I not undersell myself in this case?

Transitioning is definitely possible, and personal projects can be valuable in demonstrating your skills and passion for the field. Anecdotally, I’ve noticed that it is much easier for people to pivot whilst in the same company, your existing experience and familiarity with the company’s systems may be advantageous, and they may be more willing to consider you for the transition.

Speak with your manager or HR department to express your interest in data engineering roles. You could also look for opportunities within your current role to gain hands-on experience with data engineering tasks. Collaborate with data engineering teams on projects or volunteer to assist with data-related tasks. This can provide valuable practical experience and help you establish relationships with colleagues in the field.

Regarding seniority and pay, transitioning to a new field may involve starting at a lower level or with a lower salary, especially if you’re moving to a junior position. However, the extent of this adjustment can vary based on the company, your skills and experience, and negotiation discussions.

When discussing the transition, highlight the value and transferability of your existing skills and any additional qualifications you’ve acquired during your preparation.

Here are some tips to help you navigate this transition and position yourself effectively:

Identify the required skills: Research and understand the key skills and technologies that are essential for a data engineer role. This typically includes database management, data processing frameworks, ETL (Extract, Transform, Load) processes, and data modelling.

Acquire necessary skills: Evaluate the gaps in your current skill set and take steps to acquire the required skills. Online courses, tutorials, and certifications can be helpful resources. Consider projects or exercises that allow you to apply what you’ve learned to gain practical experience.

Build a portfolio: Personal projects related to data engineering can be a valuable addition to your portfolio. They demonstrate your ability to work with data, design and implement data pipelines, and showcase your problem-solving skills. Share your projects on platforms like GitHub or create a personal website to showcase your work.

Network and collaborate: Engage with the data engineering community through online forums, meetups, and conferences. Networking can provide you with valuable insights, mentorship opportunities, and potential job leads. Collaborating on open-source projects or joining data-focused communities can also help you learn from others and build your reputation in the field.

Tailor your resume and cover letter: Highlight relevant experiences and projects on your resume and cover letter. Emphasize any transferable skills from your backend development experience that can be applied to data engineering, such as problem-solving, analytical thinking, or working with databases. Be sure to articulate your passion for data engineering and your willingness to learn and grow in the field.

Prepare for interviews: Familiarize yourself with common data engineering interview questions and be prepared to discuss your projects and experiences in depth. Be honest about your transition and emphasize your ability to adapt, learn quickly, and apply your existing skills to the new role.

Highlight your value: When discussing your transition, emphasize the unique perspective and problem-solving skills you bring from your backend development experience. Showcase how your combination of skills and experiences can add value to the data engineering role. Avoid underselling yourself by clearly articulating your abilities, achievements, and potential to contribute to the team and organization.

Remember, transitioning to a new role takes time and effort. Stay motivated, keep learning, and be persistent in your job search. With the right approach and a strong portfolio, you can increase your chances of successfully transitioning to a data engineer role.

Do you have any advice on how to start a freelance career as a data scientist?

Getting started in a new career can be really stressful, but starting out as a freelance data scientist can be a rewarding and flexible option. Here are some tips to help you get started:

Build a strong foundation: Ensure you have a solid understanding of data science concepts, including statistical analysis, machine learning, data visualization, and programming languages like Python or R. Continue to enhance your skills through online courses, tutorials, and practical projects.

Define your niche: Identify your area of specialization within data science. This could be anything from natural language processing to predictive analytics or image recognition. Focusing on a specific niche will help you differentiate yourself and attract clients looking for expertise in that particular field.

Create an impressive portfolio: Develop a portfolio of projects that highlight your skills and capabilities as a data scientist. Choose projects that demonstrate your problem-solving abilities, data wrangling, model development, and result interpretation. Showcase your portfolio on platforms like GitHub or create a personal website to exhibit your work.

Leverage freelance platforms: Join reputable freelance platforms such as Upwork, Freelancer, or Toptal, which connect clients with freelancers. Create a compelling profile that highlights your expertise, experience, and portfolio. Start by bidding on smaller projects to gain traction and positive reviews, which will help you establish credibility and attract more clients.

Network and market yourself: Actively network with professionals in the industry, attend relevant conferences, and participate in online communities. Establishing connections can lead to referrals and collaborations. Additionally, market yourself through social media platforms, blog writing, or contributing to data science publications. Demonstrating thought leadership can help you attract clients and establish your credibility.

Develop client relationships: Provide exceptional service to your clients by clearly understanding their requirements, delivering quality work, and maintaining effective communication. Building long-term relationships with clients can result in recurring work and positive referrals.

Pricing and contracts: Determine your pricing structure based on factors such as the complexity of the project, your level of expertise, and the market rates for freelance data scientists. Use clear and concise contracts that outline project scope, deliverables, timelines, payment terms, and any additional terms and conditions to protect both parties' interests.

Continuous learning and adaptation: Stay updated with the latest trends and advancements in data science. The field is constantly evolving, and clients seek professionals who can apply the latest techniques and tools. Allocate time for continuous learning, explore new methodologies, and adapt to emerging technologies.

Reputation and reviews: Focus on building a strong reputation as a freelance data scientist. Encourage clients to provide feedback and reviews for your work. Positive reviews and testimonials will enhance your credibility and attract more clients.

Establish a Professional Brand: Develop a professional brand as a data scientist. Create a compelling personal brand statement, design a professional logo, and maintain consistency across your online presence. A strong brand can help you stand out in a competitive freelance marketplace.

If I am currently employed and don't want my current employer to know I am job hunting, what is the best way to approach this?

Your current employer finding out that you’re looking for a new role (especially if they don’t hear it from you) rarely goes well.

If you are using OfferZen for your job search you are able to block specific companies (whether they make use of the platform or not) from being able to view your profile for this very reason.

If you’re using LinkedIn, you can limit who can view that you are open to work. If you click on the “show recruiters you are open to work” on your profile under visibility you can adjust who is able to view that. However, this feature isn’t entirely useful if your current company’s HR also uses LinkedIn to source candidates.

I’m not sure about other job boards and sites, might be worth reaching out to the support team of such sites to find out how you can protect your profile from being viewed by certain companies.

If you do find yourself in a situation where your boss has found out that you are looking for work, this can be a great opportunity to have a conversation about your interests and growth opportunities at your current company.

Be upfront and honest about the space you are hoping to move into in your career.

Do you have advice for someone looking for their first job in data science after completing a data science bootcamp?

My colleague Sandrika wrote an amazing article: Landing your first job after completing a bootcamp.Here are a few key takeaways from the article to help you secure an interview and prepare to stand out during the hiring process:

Create a CV that’s passionate and full of personality
Do a SWOT analysis
Set SMART goals for yourself
Find a mentor
Prepare thoroughly for interviews
Communicate clearly

How can I improve my chances of getting invited to an interview? I already got feedback on my resume from a few people who hire data scientists or work in HR. But I keep getting rejected without a reason.

Getting invited to an interview can be a challenging process, especially if you keep getting rejected without feedback. It’s possible that the job market is competitive or that there are factors beyond your control. Here are several strategies you can use to improve your chances:

Customize your resume: Tailor your resume for each job application to match the position’s specific requirements. Highlight relevant skills, experiences, and projects that align with the job description. Use keywords and phrases from the job posting to demonstrate your suitability for the role.

Strong cover letter: Write a compelling cover letter that complements your resume. Use the cover letter to explain your passion for data science, why you’re interested in the company, and how your skills can add value to the organisation.

Networking: Leverage your network and professional connections to increase your chances of getting noticed. Attend industry events, connect with professionals on LinkedIn, and engage in conversations with people in the data science field. Networking can lead to referrals and potential job opportunities.

Portfolio and projects: Showcase your data science projects and portfolio on platforms like GitHub or your personal website. Having a strong portfolio can provide concrete evidence of your skills and experience and make you stand out to potential employers.

Targeted applications: Focus on applying for roles that closely match your skills and experience. Avoid applying to positions where you are significantly underqualified or overqualified.

Follow-up: After submitting an application, consider following up with the hiring manager or HR representative. A short email expressing your continued interest in the position can demonstrate your enthusiasm and professionalism.

Consider junior roles: Be open to applying for entry-level or junior data science positions, especially if you are early in your career or transitioning from a related field. These roles can serve as stepping stones to more senior positions.

Landing a job often requires patience and persistence, but with the right approach, you can improve your chances of securing an interview and finding the right opportunity.

We hope these answers help you navigate your next career move with confidence. If you’re looking for support that takes the guesswork out of job searching, sign up to OfferZen. Companies reach out to you with upfront role, tech stack, and salary information. You’ll also get a Talent Advisor to guide you through your job search and find the right role for you.