Data Science Career Guide

You applied to 100+ jobs but no interviews? Here's what's actually happening. Your experience is valuable. You're just invisible. Let me explain why, and how to fix it. When you apply online, your resume goes into a database called an ATS (Applicant Tracking System). Think of it like a massive filing cabinet. Now here's the key: Some recruiters don't read every resume. They search. Just like you search Google, they search their database: "Python AND data analysis" "SAFe AND agile transformation" "Tableau AND dashboard" If your resume doesn't have their exact search terms, you’re making it harder to get discovered. You're not rejected. You're just not found. But here's the secret: The job description often tells you EXACTLY what keywords they'll search for. It's like having the answer key. Example from a real job posting: If they say "Experience with Snowflake required"... → They'll search "Snowflake" → Make sure you write "Built data warehouse in Snowflake…" Not "cloud database" or "modern data platform." Use their exact words: Snowflake. I've mapped out 80 keywords that get candidates noticed in 2025: Top searches happening right now: • Python, TensorFlow, LangChain (AI roles) • Kubernetes, Terraform, Docker (tech leadership) • Power BI, Tableau, SQL (data leadership) • SAFe, Agile, DevOps (transformation roles) Your action plan: 1. Read the job description carefully 2. Circle every tool, platform, or methodology mentioned 3. Add those EXACT terms to your resume (if you have that experience) 4. Use them naturally in your accomplishments Example: Instead of: "Led team through digital modernization" You say: "Led SAFe agile transformation using ServiceNow and Jira, reducing delivery time by 40%" You have the experience. Now make it searchable. Your next role isn't rejecting you. It just hasn't found you yet. You’ve got this! 💡 Save this cheat sheet of 80 searchable keywords ♻️ Share to help someone in your network Follow me for more insider recruiting insights

Job seekers are trapped in the "need experience to get a job, but need a job to get experience" cycle. Here is how you can break it: • Gain experience using public datasets: it's not about fancy machine learning projects. Start with cleaning, aggregating, and visualizing data in tools like Excel or Python in Google Colab. Find an interesting datasets from platforms like Kaggle, or US Goverment Open Data (https://data.gov/ ), or data from your city (e.g. Seattle's real-time fire 911 calls https://lnkd.in/gNEdS9Yk). ALWAYS create an artifact—a blog post, a GitHub repository, something to showcase. • Seek opportunities near you: Your uncle is running small business? They might need data insights. Your professor might be eyeing for someone to dissect student performance data. Reach out, offer your skills. Maybe you can collect your own data on your diet or sleep, and analyze it for yourself. (Data science YouTuber Ken Jee analyzed his own health data: https://lnkd.in/gf2SWNDq) No one is offering you a job? Create a job for yourself. • Leverage your current experience: maybe you are just learning data science but you have experience in other industries like marketing, finance, etc. You might not be the best data person, but you could be the person that knows more about the industry than an average data person, and knows more about about data than the average retailer. Leverage your current domain knowledge as a stepping stone, you don't have to start over completely. In the realm of data analytics, the world is your playground. Forget the traditional paths—carve out your own. There are multiple guests on my podcast started their career in non-tech roles. Experience isn't confined to job titles; it's crafted through initiative and passion. I interviewed a career coach who got into Google from non-tech background, learn more from our conversation: Apple: https://lnkd.in/gaM_cWP9 YouTube: https://lnkd.in/gCHTU94N Spotify: https://lnkd.in/g6fGuXzP #Datascience #Career

Best way to stand out early in your data career? Think like a business owner 💡 👉 Talk to stakeholders to understand their motivations 👉 Build domain knowledge to learn the nuances of the business 👉 Clearly articulate how your analysis ties to specific goals or KPIs 👉 Draft a measurement plan before you even touch the data Early in my career all I wanted to do was build fancy reports and dashboards, but as soon as I started thinking this way everything changed. Not only did I start earning respect and recognition from management, but I began to actually see (and measure) the impact of my work. This was probably the single biggest catalyst in my career growth and development as an analyst. So to all the seasoned pros out there, what other advice would you give to help an analyst accelerate their career?

From Data Analyst to Data Scientist with a $95k offer in under 6 months without a “perfect” portfolio. Here’s what made the difference for a client of mine: 𝟭) 𝗕𝘂𝗶𝗹𝘁 𝗮 𝗥𝗼𝗮𝗱𝗺𝗮𝗽: We started with a tailored roadmap, breaking down each step into daily actions. Instead of trying to learn everything, we targeted just a few tools, each relevant to the skills and roles they were aiming for. 𝟮) 𝗚𝗿𝗲𝘄 𝗧𝗵𝗲𝗶𝗿 𝗖𝗼𝗻𝗳𝗶𝗱𝗲𝗻𝗰𝗲: This client often felt behind when comparing their skills to others. We focused on what they already excelled at, pushing them to apply and interview before they felt “ready.” With each attempt, confidence grew, a reminder that nothing reinforces skill like action. 𝟯) 𝗡𝗶𝗰𝗵𝗲𝗱 𝗧𝗵𝗲𝗶𝗿 𝗕𝗿𝗮𝗻𝗱: Previously, their profile was too broad. We focused their portfolio on health care and NLP, then optimized it with keywords that attracted the right employers. Employers started noticing, even before they’d wrapped up their main project. 𝟰) 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆 & 𝗕𝗿𝗲𝗮𝗸𝗶𝗻𝗴 𝗧𝗵𝗿𝗼𝘂𝗴𝗵 𝗦𝗲𝘁𝗯𝗮𝗰𝗸𝘀: They’d had a pattern of starting courses, then quitting. Through daily check-ins, clear action steps, and real-time feedback, they overcame self-sabotage and kept momentum even when it was tough. 𝗥𝗲𝘀𝘂𝗹𝘁𝘀? - 6 months of targeted effort, with 4 focused on skill-building - 50 applications, 3 interviews, and 1 offer This transition didn’t happen by waiting for “perfect.” It happened by taking daily action, building on existing skills, and niching their value. If you’re waiting to feel “ready,” this is your reminder: take the first step today. What’s one skill you want to strengthen next?

I cracked jobs at Amazon, Microsoft, and TikTok, using this roadmap. Now it's your turn to use it. Every week, I speak to job seekers who say things like: “I’ve applied to 200+ jobs but barely got a response.” And I get it. Mass applying feels productive, but it usually leads to mass rejection When I was job hunting, I knew I had to play the game differently. Here’s what actually helped me land offers from top companies: 1. Find the right role I didn’t waste time scrolling endlessly on job boards. Instead, I went straight to the official careers pages. For Meta: metacareers dot com I set job alerts and searched for specific roles like: Software Engineer - Machine Learning - New York Not just “Software Engineer.” Bonus tip: I kept an eye on hiring managers’ posts — they often hint at open roles before they’re listed. 2. Apply the right way I applied within 24–48 hours of a role going live. Early birds really do have an edge. I tailored my resume to include the right keywords from the job description (ATS optimization is non-negotiable). And I didn’t hit “Apply” unless I was also working on finding a referral. 3. Make recruiters notice me Before I reached out, I fixed my LinkedIn: ✅ Clear headline ✅ Strong featured section ✅ Keywords that matched my target roles I turned on “Open to Work” (visible only to recruiters) And started engaging with recruiters’ posts before sending a DM. 4. Network like a PRO I searched for people who had recently joined these companies. Commented on their posts. Then sent personalized DMs like: “Hey [Name], I came across your work at Meta — really insightful! I’m exploring roles in [XYZ] and would love to learn more about your experience. Open to a quick chat?” No cold asks, but real conversations. 5. Prepare like I already have an interview I tracked questions in Notion. Did mock interviews on Interviewing(dot)io and Pramp. And I worked with a coach to tighten up my stories and delivery. Within a few days of following this strategy, I landed multiple interviews with the top companies. Save this post if you’re job hunting right now. P.S. Follow me if you are an Indian job seeker in the U.S. I talk about job search, interview prep, and more.

I’m 53. I've been doing analytics for 13+ years. Here are 8 no-BS steps I've learned to build DIY data science skills: 1) Crawl-Walk-Run Social media would lead you to believe that you must: Work with gigantic datasets. Use deep neural networks, LLMs, etc. Be an advanced mathematics wizard to do anything. It's simply not true.  Here's what you do... 2) Crawl - learn decision trees The dirty little secret of business analytics is that you usually use simple techniques. Prime example - decision tree machine learning: Works excellent with data tables. You can learn them using your intuition. When you're ready, you can learn the math. 3) Crawl - Use simple datasets When you first start, do yourself a favor and use simple datasets. While they get a lot of hate on social media, you want to concentrate on how decision trees learn from data. That's much easier when you use simple datasets. Worry about data wrangling later. 4) Walk - build ML fundamentals Once you build your intuitive understanding of decision trees, it's time for building skills with: Data profiling Feature engineering The bias-variance tradeoff Tuning decision tree models With these skills you're ready for... 5) Walk - the mighty random forest As the name suggests, random forests are collections of decision trees. In ML, this is known as an "ensemble." Ensembles of decision trees are state-of-the-art for real-world DIY data science. Next up, it's about the data. 6) Walk - data wrangling "Data wrangling" is a term for all the work needed to prepare data for DIY data science. Here's the thing, though. Building data wrangling skills is much easier when you know ML. This knowledge provides the context for what you need to do with the data. Moving on... 7) Run - apply it at work While I'm a big fan of using Kaggle to build initial skills for crawling and walking, Nothing beats applying what you've learned at work. Even if nobody ever sees it, the experience you will build is invaluable. P.S. - Don't do anything that will get you fired. 8) Run - expand With some work projects under your belt, it's time to expand your skills. Start with cluster analysis: K-means DBSCAN A powerful combination for DIY data science is cluster analysis + ML models for interpretation. Be sure to use this combo at work! Ready to build DIY data science skills? Join 6,175 professionals learning Python and ML with my free crash courses: https://lnkd.in/e7fVrjxC

The Hard (and Surprisingly Popular) Way to Fail at Getting into Data Science: 1. Start by watching endless tutorials on every data-related topic, hoping the knowledge sticks through osmosis. 2. Panic after a couple of rejections and consider switching to a completely unrelated field—dog grooming, maybe? 3. Assume your resume will do the heavy lifting while completely ignoring the power of networking (spoiler: networking > resume). 4. Chase the next trendy tool like it’s a magic wand, without building a solid foundation in engineering or math. 5. Follow the crowd, focusing on what’s “hot” instead of what actually interests you, and end up with a cookie-cutter portfolio. 6. Apply to anything with “data” in the title, even if it’s an admin job or involves staring at spreadsheets all day. 7. Stuff your resume with buzzwords like “Spark” and “Big Data” even though the closest you’ve come to using them is reading a Medium article. 8. Set an unrealistic timeline: “If I’m not hired in six months, I’m throwing in the towel.” 9. Blame the universe for every rejection instead of adjusting your game plan. A Better, Smarter Approach to Breaking into Data Science: 1. Choose your adventure. Focus on areas that genuinely pique your interest—whether it’s NLP, computer vision, or something else that gets you excited. 2. Make networking your superpower. Building relationships with people in the industry can open doors you didn’t even know existed. 3. Learn from actual professionals. Forget just instructors—talk to people already doing the job to find out what skills they really use. 4. Work on projects that matter to you. When you’re passionate about a problem, your project will naturally stand out. 5. Find a mentor early. A good mentor can fast-track your learning and help you avoid costly mistakes. 6. Share your learning journey. Post regularly about what you’re working on, and you’ll build a community that supports you. 7. Consistency beats burnout. Slow and steady progress is better than trying to cram everything into a few intense weeks. 8. Get real-world experience early. Whether it’s freelancing, internships, or contributing to open-source projects, applying your skills is key. 9. Play the long game. Breaking into data science is a marathon, not a sprint. Persistence is what separates those who make it from those who quit too soon. Bottom Line: It’s about enjoying the process, learning along the way, and staying the course. There’s no magic formula—just perseverance and patience.

A lot of people trying to break into data science spend months, sometime even years... Learning the wrong things. They dive deep into neural networks, reinforcement learning, and complex machine learning algorithms, thinking that’s what will land them a job. But when they finally start applying, they realize the job market is looking for something else. So... what do companies want then? Most companies hiring data scientists aren’t looking for cutting-edge AI research. They need professionals who can: + Work with messy, real-world data – Cleaning, structuring, and analyzing data is 80% of the job. If you can’t handle raw datasets, machine learning skills won’t matter. + Use SQL fluently – If you can’t query a database efficiently, you’ll struggle in almost any data role. SQL is still one of the most in-demand skills in the field. + Apply basic statistical thinking – Companies don’t need fancy deep learning models for most problems. They need people who understand probability, regression, and how to make sense of data. + Communicate insights effectively – Data scientists who can translate numbers into clear, actionable recommendations will always be more valuable than those who just build models. + Understand the business problem first – Companies care about ROI, not algorithm complexity. If you don’t connect your work to business impact, you’ll be seen as just another technical hire. So... what mistakes are people doing? - Overloading on Theory Without Application – Learning every ML algorithm but never actually working on real datasets. - Ignoring SQL and Data Wrangling – Machine learning is useless if you can’t efficiently extract and clean data. - Building Portfolio Projects With No Business Impact – Instead of copying Kaggle projects, focus on solving problems that could help a company save money, improve efficiency, or make better decisions. How would I approach it? 1. Master SQL and data manipulation before diving into machine learning. 2. Prioritize problem-solving with real business datasets, not just pre-cleaned Kaggle data. 3. Learn to present insights clearly and tell a compelling data story. Focus on building projects that demonstrate impact, not just model accuracy. The data science job market isn’t looking for people who know the latest AI trends—it’s looking for people who can solve real problems with data. If you’re trying to break into the field, ask yourself: Are you learning what actually matters, or just what looks impressive on paper? Would love to hear your thoughts.

Don’t let a lack of experience hold you back from pursuing a data science career… Before I broke into data science, I was constantly learning new things but didn’t have much work experience to show for it. Here’s what I did: 1. Built a portfolio I worked on personal projects to showcase my skills. 2. Joined online communities Networking with others in the field helped me understand the industry better and opened up opportunities. 3. Practiced coding interviews I spent time solving problems and practicing coding challenges. This was crucial in helping me get comfortable with technical interviews. 4. Highlighted my learning journey During interviews, I spoke about my dedication to learning and the projects I completed. ✨ My projects and learning outside of my data Bootcamp landed me my first job. There’s always another way to show your value. Don’t give up easily! P.S. What other strategies have you used to overcome the lack of experience in your job search? #DataSistah ♻️Reshare and help others.

Too many data and AI teams aren’t tied to a critical business function or revenue-generating product. In the rush to cash in on going AI-first, businesses forgot to select impactful projects for teams to work on. Data scientists and engineers can spend years working in one of these teams, but have little to show for it aside from a few “special projects” with no connection to business impacts. It’s like having a black hole on your resume, and it makes finding a new job more difficult. When I talk to people who aren’t having success on the job market, they’re typically escaping a zero-impact team or role. The solution starts with technical leadership. If your team isn’t directly connected with business impacts or you can’t quantify those impacts, do this ASAP: Evaluate the projects that are in progress and on the roadmap. Do any of them touch revenue-generating products or core business functions? Implement a value-based prioritization model. Shelve anything that doesn’t have a quantifiable impact. Promote projects that touch KPIs that C-level leaders are paying attention to. Put projects that could generate revenue at the front of the line. Advertise what you’re doing and why to the C-Suite. Don’t be bashful because this could save your team from the chopping block. No one will be upset at you for reprioritizing based on value and impact. Reskill team members with domain expertise by embedding them with product teams and high-profile business units. Create shifts of one week embedded and three weeks with the data and AI team. Rotate team members so everyone gets exposure. If you can, bring in a product manager to help work with external teams, rebuild the roadmap, and implement value-based prioritization. If you can’t, take on as much of that as you’re capable of. When you run into gaps, advertise the impact of not having someone in the value management role so you can get help. Doing two jobs isn’t sustainable for very long. Finally, don’t wait for the right time or for executive leaders to be more receptive. Every team that doesn’t contribute to the top and bottom line is under scrutiny and at risk.