Tips for Overcoming Data Career Challenges

Looking back, I made a lot of mistakes in my data science journey. If I had to start over, here’s what I’d do differently—so you don’t have to make the same mistakes. 1. Stop Learning Everything & Focus on What Actually Matters When I started, I thought I had to learn every single ML algorithm, master deep learning, and get into reinforcement learning just to land a job. -> Reality? I barely needed any of that in my first role. What actually mattered: ✅ SQL – Used daily, and the most underrated skill in data science. ✅ Python & Pandas – Not just writing code but actually understanding how to work with messy real-world data. ✅ Data Storytelling – If you can’t communicate your insights, your work doesn’t matter. -> Instead of chasing every new trend, I would have focused on strong fundamentals early on. 2. Stop Collecting Certificates & Start Building Projects I used to think more certificates = better job prospects. So I took courses, completed certifications, and added every badge I could find to my LinkedIn. -> Guess what? Not a single recruiter ever asked about them. What actually made a difference: ✅ Building real-world projects that solve problems ✅ Documenting and explaining my work like a case study ✅ Having a GitHub/portfolio that showcases practical skills -> Certificates can be helpful, but they won’t replace actual experience—even if that experience comes from self-initiated projects. 3. Start Networking Way Earlier For too long, I thought I could just apply online and get hired. So I focused on resumes, cover letters, and grinding through applications. -> What I didn’t realize? 🚨 Most jobs are filled through referrals and networking. 🚨 Many roles are never even posted publicly. If I had to start over, I would have: ✅ Attended local meetups and conferences earlier ✅ Engaged on LinkedIn, not just scrolled ✅ Asked for informational interviews with industry professionals -> One conversation can open more doors than 100 cold applications. 4. Learn the Business Side of Data Science Sooner At first, I focused purely on the technical side—writing the best code, getting the highest model accuracy, optimizing algorithms. -> What I didn’t realize? No one cares about a 0.1% model improvement if it doesn’t drive business value. Companies don’t hire data scientists to build models. They hire them to solve business problems. ✅ Understanding the industry and domain is just as important as technical skills. ✅ If you can tie data insights to business impact, you become invaluable. The Biggest Lesson? -> I spent too much time learning things I never used and not enough time on things that actually mattered. If I could start over, I’d focus on practical skills, networking, and solving real problems from day one. If you could restart your career, what’s one thing you’d do differently?

Do you feel stuck in your data job search but don’t know the problem? As a Data mentor for the last 3 years, helping over 100 people 1:1 and having gone through it myself, here are the four main problems I find: Problem 1: Roadmap: Lack of Skills or the Path to Get Them Symptoms: - Unclear on the required skills or qualifications. - Uncertain of your strengths and weaknesses. - Lack of marketable projects or hands-on experience. Steps: 1) Assess Your Skills: Match 40% of your skills to job descriptions for your desired role. 2) Identify Gaps: Recognize your strengths and weaknesses. 3) Build Projects: Create industry-level projects to showcase your skills. Problem 2: Marketing: Lacking Visibility Symptoms: - Have the necessary skills but struggle with profile traction. - Some recruiter outreach or screenings, but not enough interest. Steps: 1) Enhance Your Portfolio: Add impact and value to your LinkedIn, resume, cover letter, GitHub, and website. 2) Optimize for Readability: Ensure it’s human-readable and optimized for ATS and SEO. 3) Make It Unique: Stand out with unique content. 4) Create Content: Regularly produce content to showcase your expertise. Problem 3: System: Inconsistent Interview Opportunities Symptoms: - Few or no interviews, and they’re not for desirable positions. - Primary strategy is applying online. - Lack of networking or referral strategies. Steps: 1) Leverage Your Network: Ask friends and family for referrals. 2) Target Companies: List 10-15 companies you want to work for. 3) Find Contacts: Identify 10-20 people from each company. 4) Build Relationships: Network and build genuine connections. 5) Ask for Referrals: Request referrals from your connections. Problem 4: Interviews: Limited or No Offers Symptoms: - Getting interviews but not offers. - Struggling with specific interview types. - Unable to showcase impact. - Offers don’t meet your expectations. Steps: 1) Highlight Your Strengths: Know your key achievements and skills. 2) Understand the Process: Learn what each interview round focuses on and how to succeed. 3) Improve Communication: Practice asking questions, using positive body language, and making it conversational. 4) Daily Practice: Continuously practice your interview skills. Mock Interviews: Conduct mock interviews to refine your technique. Conclusion Identify where you’re stuck and take actionable steps to move forward. What strategies have helped you move to the next problem in your job search? Share your tips in the comments below! ------------------------- ➕ Follow Jaret André for more daily data job search tips. 🔔 Hit the bell icon to be notified of job searchers' success stories.

Newsflash: working on data teams is a thankless job. So much of your work will be underneath the surface. Most of what you do will neither be praised nor understood by business stakeholders But don’t ever let that stop you from ALWAYS striving to prove your business value above all else Case in point: I had recently spent over a month developing frameworks and data models for a series of A/B tests a customer wanted to conduct using Narrator During that time, I had very few updates for them because so much of the work involved cleaning up their HUGE volumes of daily data, modeling it, and validating the model before it became even remotely usable for their experimentation use cases or even presentable to stakeholders Each week they’d check in for a status update, a new data issue or feature request from the stakeholders would pop up, so even more time had to be spent behind the scenes to ultimately deliver the simple, nicely-packaged deliverable that really did not do all the work justice So here are my tips on how to keep your contributions visible in a role that is often invisible: 👉 Align with business stakeholders on their metrics for success upfront 👉 Provide regular updates to them so they know you’re not actually slacking (duh) 👉 Be proactive in anticipating stakeholders’ needs and reactions to problems that will arise along the way (e.g. if you know they’ll request a common type of feature, implement it first and ask them if they actually need it later) 👉 Always build in buffer time for your final deliverable 👉 If your timelines still go beyond that time allotted, manage stakeholder expectations by realigning on step 1 But in my experience, a thankless job is not necessarily a joyless one. Not all superheroes wear capes, but some of them might just be silently keeping your business afloat, one data pipeline at a time #data #careers #datascience