How to Land Entry Level Data Roles

Trying to land your first data job but feel stuck in “learning mode”? You’re not alone. Most new analysts spend months on courses without knowing what hiring managers actually care about.  After years helping professionals break into data, here’s what I’ve learned:  Skills don’t speak for themselves, 𝘰𝘶𝘵𝘱𝘶𝘵𝘴 do. If you’re just starting out, here’s the fastest way to build trust with recruiters (even without experience): 𝗦𝘁𝗼𝗽 𝗳𝗼𝗰𝘂𝘀𝗶𝗻𝗴 𝗼𝗻 “𝘄𝗵𝗮𝘁 𝘆𝗼𝘂’𝗿𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴.” 𝗦𝘁𝗮𝗿𝘁 𝘀𝗵𝗼𝘄𝗶𝗻𝗴 𝘄𝗵𝗮𝘁 𝘆𝗼𝘂 𝗰𝗮𝗻 𝗱𝗼 𝘄𝗶𝘁𝗵 𝗶𝘁. That means: – Create one-page projects that answer real business questions  – Use tools you’re learning (SQL, Excel, Power BI, Python) to clean messy data  – Share insights in plain English don’t hide behind dashboards  – Post consistently and narrate your process like a consultant would You don’t need 10 certificates. You need 3 solid case studies that show how you think. 📌 If you’re targeting analyst roles, aim to solve:  ➝ How can we increase customer retention? ➝ Where are we losing money? ➝ What product is underperforming? These aren’t just data questions. They’re business problems solved with data thinking. You won’t master everything at once. But you can show you're learning like a pro. 𝗧𝗵𝗲 𝗱𝗮𝘁𝗮 𝗳𝗶𝗲𝗹𝗱 𝗿𝗲𝘄𝗮𝗿𝗱𝘀 𝗮𝗰𝘁𝗶𝗼𝗻, 𝗻𝗼𝘁 𝗽𝗲𝗿𝗳𝗲𝗰𝘁𝗶𝗼𝗻. 𝗠𝗮𝗸𝗲 𝘆𝗼𝘂𝗿 𝘀𝗸𝗶𝗹𝗹𝘀 𝘃𝗶𝘀𝗶𝗯𝗹𝗲. 𝗧𝗵𝗮𝘁’𝘀 𝗵𝗼𝘄 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱 𝘁𝗿𝘂𝘀𝘁.

A job seeker came to me after 3.5 months of job searching with the following data: 180 applications submitted 12 screenings 1 referral 5 interviews 1 final round 0 offers After reviewing the data, I found that their job search was actually performing well in some areas but had key bottlenecks: - Strong application-to-screening rate Their resume and portfolio were doing well, getting them past the initial stage. - Good screening-to-interview rate Their performance in behavioral and situational questions was above average. - Weak interview-to-final round conversion  This indicated a struggle with: Technical rounds – Not demonstrating enough depth in core skills. Alignment with job descriptions – Answers weren’t tailored to the company’s needs. Surface-level responses – Not showcasing impact or real-world application of skills. The plan to improve: If I were coaching them, I’d focus on three key strategies: 𝟭) 𝗗𝗲𝗲𝗽 𝗜𝗻𝘁𝗼 𝗜𝗻𝘁𝗲𝗿𝘃𝗶𝗲𝘄 𝗦𝗸𝗶𝗹𝗹𝘀 Develop an interview strategy to explain technical and soft skills in-depth. Relate answers directly to the job description and company goals for higher impact. Use structured responses like the STAR method, but emphasize impact and problem-solving. 𝟮) 𝗜𝗻𝗰𝗿𝗲𝗮𝘀𝗲 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲 Daily practice of technical questions tailored to their target roles. Mock interviews to simulate real-world scenarios. Feedback loops to refine and improve responses. 𝟯) 𝗕𝗼𝗼𝘀𝘁 𝗥𝗲𝗳𝗲𝗿𝗿𝗮𝗹 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 Increase outreach to professionals in their industry. Leverage networking and informational interviews to gain more referrals. Prioritize companies where referrals hold more weight. Key Points: ✔️ Data-driven job search analysis helps pinpoint areas that need improvement. ✔️ Fixing interview bottlenecks is often the key to securing more final rounds and offers. ✔️ Referrals still matter even in markets where they aren’t as strong as in the US or Canada. ✔️ Daily practice and structured preparation make a big difference in interview performance. By focusing on these areas, They could significantly increase their final round conversions and land a job faster. Have questions about your job search or how to break into data roles? Drop them in the comments, or send me a message. Let's get you to your next role! ------------------------ ➕Follow Jaret André  for more daily data job search tips.

Here is what I would do if I had to land my first Data job again: 𝗦𝘁𝗲𝗽 𝟭: 𝐏𝐫𝐚𝐜𝐭𝐢𝐜𝐞 𝐟𝐨𝐫 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰𝐬 - Build a strong SQL foundation and master advanced queries - Learn to answer product and business questions - Practice behavioral interview responses - Learn Pandas functions in Python For this, I highly recommend these resources: 📘 Product Sense: https://lnkd.in/eBfkadAd 📔 SQL Course: https://lnkd.in/eGWcaYxK 📕 SQL Interview Qs: https://lnkd.in/eDMic4tR 📗 Python Interview Qs: https://lnkd.in/e_7BaKeC And make sure to research the company and understand their business. 𝗦𝘁𝗲𝗽 𝟐: 𝐁𝐮𝐢𝐥𝐝 𝐲𝐨𝐮𝐫 𝐍𝐞𝐭𝐰𝐨𝐫𝐤 Get active on LinkedIn. Follow data experts and companies you’re interested in and learn from them! Here is my list of my favorite high quality creators: - Sai Kumar for SQL/Python & job search tips - Mandy Liu - for all Data Science content - Chris Perry - for SQL & Tableau - Megan - 🤩 video content On top of that, make sure to have 1 mentor - that you're aspired to - that will guide through your journey. I know it's hard to find (especially for free!) but it's doable. And if you have the money, it's 1000% worth it! 𝗦𝘁𝗲𝗽 𝟑: 𝗔𝗽𝗽𝗹𝘆, 𝗔𝗽𝗽𝗹𝘆, 𝗔𝗽𝗽𝗹𝘆! - Optimize your Resume using this template: https://lnkd.in/eKhiGFaG - Build an Application tracker: this is really important to see your progress - Reach out to friends or network for referral - this will get you a higher chance to land that first interview - Cold outreach: this one is the least used of all but definitely WORKING! So find the hiring manager/recruiter email and reach out to them directly. 𝗦𝘁𝗲𝗽 𝟒: 𝐌𝐚𝐤𝐞 𝐏𝐫𝐨𝐣𝐞𝐜𝐭𝐬 Only applying is not fun - so continue building your data muscles by: - Making 3-5 solid projects using free datasets from Kaggle. - Sharing them on Maven Analytics - best platform for building your portfolio Voila. Take these steps and I'm confident you will land your 1st data job. You got this!

My advice to new grads in data (after 1000+ DMs from them) Different backgrounds. Different countries. But the same 5 questions, every single time. I keep seeing the same roadblocks. Here’s how to break past them: 1. 𝐃𝐨𝐧’𝐭 𝐰𝐚𝐢𝐭 𝐭𝐨 𝐠𝐞𝐭 𝐡𝐢𝐫𝐞𝐝 𝐭𝐨 𝐬𝐭𝐚𝐫𝐭 𝐝𝐨𝐢𝐧𝐠 𝐝𝐚𝐭𝐚 𝐰𝐨𝐫𝐤. → Start now. Pick a dataset. Find a question. Answer it. → You learn by doing, not just watching videos. 2. 𝐘𝐨𝐮𝐫 𝐫𝐞𝐬𝐮𝐦𝐞 𝐬𝐡𝐨𝐮𝐥𝐝 𝐭𝐞𝐥𝐥 𝐚 𝐬𝐭𝐨𝐫𝐲, 𝐧𝐨𝐭 𝐣𝐮𝐬𝐭 𝐥𝐢𝐬𝐭 𝐬𝐤𝐢𝐥𝐥𝐬. → If it says “SQL, Python, Tableau”… that’s not a story. → Show how you used them to solve a real problem. 3. 𝐏𝐢𝐜𝐤 1-2 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐚𝐧𝐝 𝐠𝐨 𝐝𝐞𝐞𝐩. → Not 10 shallow ones. → One solid project, clearly explained, can beat a bootcamp certificate. 4. 𝐉𝐨𝐛 𝐭𝐢𝐭𝐥𝐞𝐬 𝐝𝐨𝐧’𝐭 𝐦𝐚𝐭𝐭𝐞𝐫 𝐞𝐚𝐫𝐥𝐲 𝐨𝐧. → It doesn’t have to say “Data Analyst.” → Look for analyst roles, marketing ops, product insights, any role where you get to work with data. 5. 𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧 > 𝐣𝐨𝐛 𝐛𝐨𝐚𝐫𝐝𝐬. → Most new grads apply silently. → The ones who post, connect, and ask smart questions? They get noticed. You don’t need perfect grades, a referral, or a fancy certification. You need proof that you can work with data and communicate clearly. Remember, you don’t need permission to start. The tools are free. The knowledge is out there. The hardest part? Starting. Start messy. Start scared. But start anyway. You've got this 💪 ♻️ Save it for later or share it with someone who might find it helpful! 𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 13,000+ readers here → https://lnkd.in/dUfe4Ac6

Everyone wants to break into data. But most are focused on just one piece of the puzzle. Here’s what I’ve learned after helping dozens land roles in data (and doing it myself): 1. Technical skills will get you through the door. SQL. Python. Excel. Power BI. But being “technical” alone isn’t enough. 2. Soft skills move you forward. Can you communicate findings clearly? Can you translate numbers into narratives? 3. Networking opens unexpected doors. Most offers don’t come from cold applications. They come from conversations. 4. Motivation is your long-term advantage. Because the journey is hard. But if you stay consistent, you’re unstoppable. It’s not about mastering one area. It’s about stacking strengths until you break through. If you’re just starting your journey into data: Build all four pillars. Which pillar are you focused on right now?

Finding your first job as a data scientist can be tough, but there's a path for everyone. When I moved from India to the U.S. for my master's degree, I had to face this challenge head-on. If you didn't land a spot in CS, IT, or Data Science degrees from Ivy leagues, don't worry. It's still possible to kickstart your career. Start as a data analyst. Many people think they need to hit a home run their first time out, but starting small can be powerful. Companies are always in need of analysts who can interpret data and provide insights. Consider joining smaller companies. They often provide more opportunities to wear multiple hats and grow your skill set. Plus, you might find close-knit teams and a culture that encourages learning. Networking is key. Platforms like LinkedIn are gold mines for new connections and job opportunities. Don't be shy about reaching out to people in the industry. Their advice and support can be invaluable. Take electives outside your main coursework. If your program allows, jump into classes related to data science. Broadening your knowledge base can open many doors. Compete on platforms like Kaggle. Participating in challenges not only sharpens your skills but also increases your visibility in the data science community. In short, pathfinding is about combining strategy with hustle: - Start as a data analyst - Look for roles in smaller companies - Network on LinkedIn - Take useful elective courses - Participate in Kaggle challenges Each step brings you closer to your goal. Remember, everyone's journey looks different, but persistence and strategy will get you there. You've got this! #datascience #careers #firstjob #mentor #guide #leadership #kaggle #datascientist #motivation