Navigating Data Careers

If you’re preparing for a Data Science role, here is a roadmap that will help you 👇 The expectations in 2025 are different. It’s not just about knowing models - it’s about systems, business value, and being production-ready. Here’s what to focus on: 1. Stay Current with the Latest Frameworks & Tools →Deep Learning: PyTorch 2.x, TensorFlow 2.x, JAX →MLOps & Data Engineering: Kubeflow / MLflow, Apache Airflow & Spark, Ray → Generative AI & LLM: Hugging Face Transformers and other opensource models, LangChain, LlamaIndex, AutoGen, CrewAI, LangGraph 💻 Resources: ↠ Fast.ai Course: https://course.fast.ai/ ↠ Kubeflow Docs: https://lnkd.in/dw6pr5E4 ↠ Hugging Face Tutorials: https://lnkd.in/dgj7NJHT ↠ Ray Docs: https://lnkd.in/dZ-iyzrd 2. Understand the Typical Interview Rounds ⦿ Initial Technical Screen → Coding Challenge: Python (or R/SQL) focusing on data manipulation, algorithms, or quick problem-solving. → Statistical & Probability Foundations: Expect questions on hypothesis testing, confidence intervals, A/B testing, Bayesian inference. ⦿ ML & Analytics Case Study → Data Analytics: Exploring real-world scenarios (e.g., user retention at a streaming company). → Machine Learning Model Discussion: How would you approach feature engineering, model selection, and evaluation? ⦿ ML System Design → Architecting Scalable Pipelines: Data ingestion, model training, evaluation, deployment, and monitoring. → Trade-offs: Discuss latency, real-time vs. batch predictions, cost-effectiveness, and reliability. ⦿ Behavioral & Team Fit → Past Projects: Deep dives into your approach, impact, and lessons learned. → Collaboration & Communication: How you handle feedback, lead cross-functional teams, and present insights. 💻 Resources: ↠ ML System Design by Alex Xu: https://amzn.to/4bdsjUR ↠ LeetCode: https://leetcode.com/ ↠ StrataScratch: https://lnkd.in/dpJg9cKf 3. Leverage a Business Value Framework →Objective: Identify key metrics (e.g., revenue, user retention). →Feasibility: Data readiness, engineering constraints. →ROI: Quantify impact; balance complexity vs. gains. →Trade-offs: Speed vs. quality, cost vs. performance. 💻 Read: https://lnkd.in/durKR-uP 4. Tackle Case Studies with Structure → Clarify the Problem: Scope, business goals, data needs. → Identify Key Metrics: Conversion, churn, retention. → Propose a Solution: Data pipeline, model type, validation. → Discuss Pitfalls: Data quality, ethical issues, costs. 5. ML System Design & Tools → Data Ingestion & Processing: Apache Airflow, Apache Spark, Kafka → Model Development & Experimentation: PyTorch, TensorFlow, Hugging Face → MLOps: MLflow, Weights & Biases, Kubeflow, DVC → Deployment & Inference: Fireworks AI, Hugging Face Endpoints, AWS SageMaker, GCP Vertex AI, Docker/Kubernetes → Monitoring & Maintenance: Prometheus/Grafana, Evidently AI, Arize AI, Seldon Core

Want to break into a data analyst role? Use your current job as a training ground! Here is how you can prepare for your transition in your daily work: 1. 𝗨𝘀𝗲 𝗗𝗮𝘁𝗮 𝘁𝗼 𝗠𝗮𝗸𝗲 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 Data is everywhere, no matter your current role. Start by using spreadsheets to track performance metrics or identify trends. Show that you can use data to support your decisions. 2. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗥𝗲𝗽𝗲𝘁𝗶𝘁𝗶𝘃𝗲 𝗧𝗮𝘀𝗸𝘀 Use Excel formulas, Power Query, or basic Python scripts to automate repetitive tasks, freeing up your time and building valuable data manipulation skills. 3. 𝗩𝗼𝗹𝘂𝗻𝘁𝗲𝗲𝗿 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀 Look for opportunities within your company to work on data-related projects. It could be assisting a colleague with a report, or helping analyze customer data. These projects give you hands-on experience that you can add to your resume. 4. 𝗟𝗲𝗮𝗿𝗻 𝗳𝗿𝗼𝗺 𝗖𝗼𝗹𝗹𝗲𝗮𝗴𝘂𝗲𝘀 If your company has a data team, try to reach out to them. Ask if you can shadow or assist on small tasks. Learning directly from analysts will help you understand the real challenges they face and expand your network. Try to find an analyst who is willing to become your mentor. 5. 𝗕𝘂𝗶𝗹𝗱 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 If you create reports or present information in your current role, practice your data storytelling skills. Use Power BI, Tableau, or Excel to visualize data in a clear, and easily digestable way. 6. 𝗧𝗮𝗸𝗲 𝗔𝗱𝘃𝗮𝗻𝘁𝗮𝗴𝗲 𝗼𝗳 𝗖𝗼𝗺𝗽𝗮𝗻𝘆 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 Many companies offer training and courses. Check if there are any analytics, Excel, or SQL courses available. Some companies will even reimburse external online lectures or full degrees. 7. 𝗖𝗼𝗻𝗻𝗲𝗰𝘁 𝘄𝗶𝘁𝗵 𝗦𝘁𝗮𝗸𝗲𝗵𝗼𝗹𝗱𝗲𝗿𝘀 Data analysts spend a lot of time understanding business needs. Practice working closely with different stakeholders in your current job. Try to understand their goals, challenges, and how you can help solve their problems using data. Start preparing for your transition to a data role right where you are! In our data-driven world, almost every position offers you the chance to practice the necessary data skills. Have you transitioned into data from another role, or are you planning to? I'd love to hear your experience! ---------------- ♻️ 𝗦𝗵𝗮𝗿𝗲 if you find this post useful ➕ 𝗙𝗼𝗹𝗹𝗼𝘄 for more daily insights on how to grow your career in the data field #dataanalytics #datascience #jobtransition #careertransition #careergrowth  

Our client pivoted from Sales to Data Analytics. They did it with no formal data experience. Here are 6 strategies they used to make it happen: Context: When our client reached out, they were stuck. They had spent months applying to data analyst roles with no success, despite completing a data analytics course. They had even received a verbal offer that was later rescinded. Frustration was building, and they were considering a return to account management. We teamed up with them, and things started to change: 1. They Clarified Their Target Role Before working with us, their approach was to just apply to any and every data analytics role that popped up. We helped shift that mindset to focus more of our energy on a smaller set of highly-aligned companies. They used this clarity to create a “Match Score” for each opportunity—filtering out roles that didn’t align with their ideal job. 2. They Optimized Their LinkedIn For What Employers Wanted To See Before joining, they weren’t getting any outreach for roles on LinkedIn. We revamped their LinkedIn headline and profile to include keywords specific to the Data Analytics space as well as projects that illustrated their capabilities. Then the inbound messages began to roll in. 3. They Shifted Their Time From Online Apps To Networking Instead of just applying online, they reached out to alumni from an analytics bootcamp they attended. They specifically focused on people who had successfully transitioned into data roles. One alum gave them insider insights into the hiring process at a target company and even suggested key skills to emphasize their application. 4. They Built A Consistent Outreach System They started sending 5 personalized LinkedIn messages per day to data professionals. They focused on asking for advice, then taking action on it and using it to open the door for a follow-up. This helped build rapport and trust, which led to multiple referrals and interviews. 5. They Went Deep On Interview Prep They knew that other candidates would likely have more “traditional” experience to lean on, so they went deep on interview prep. For technical interviews, they built a portfolio project analyzing Airbnb data to showcase SQL and visualization skills. For behavioral interviews, they prepared answer examples that tied directly into the company’s biggest needs and goals. 6. They Stayed Persistent & Flexible Originally, the recruiter who reached out was asking about a business analyst role. After pitching their SQL and Python skills, our client convinced the recruiter to get them in the door for a data analytics position. Then they used their networking to gain insider info on goals and challenges which they pitched in their interview. That approach secured the offer.

3 career insights for aspiring CAs & MBAs: How to pivot strategically From EY’s tax advisory to ISB, then Citi’s credit risk team, and now managing global credit reviews at JPM across commercial & investment banking, my career has been anything but linear. Here’s what these transitions taught me—and how you can apply them to your own journey: 1. Leverage Your CA/MBA as a Swiss Army Knife ❗️The trap: Assuming your degree locks you into one path (e.g., "CA = audit or tax forever" or "MBA = consulting or bust"). ✅️ The fix: Treat your qualifications as tools to solve broader problems. CA gave me rigor in risk assessment, while the MBA taught me to contextualize credit decisions within macro trends. At Citi, I combined both to bridge credit risk with business strategy. 2. Nonlinear Moves Require Proactive Storytelling ❗️The trap: Letting recruiters dismiss your shifts as "random" (e.g., tax → credit risk). ✅️ The fix: Frame your career narrative around transferable themes. When I moved from EY to Citi post-MBA, I highlighted how tax advisory honed my ability to decode borrower and regulatory complexity—a skill critical for assessing loan portfolios. At JPM, I leverage my cross-sector exposure at Citi for evaluating global financing and risk frameworks. 3. Global Roles Demand "Zoom In/Zoom Out" Thinking ❗️The trap: Getting lost in granular details (e.g., financial models) without linking them to big-picture risks. ✅️ The fix: Practice translating technical work into strategic insights. In my current role, reviewing credit for both commercial and investment banking means balancing sector-specific risks (zoom in) with systemic trends like geopolitics or rate cycles (zoom out). ➡️ A Hard Truth: Career pivots will invite skepticism—whether it’s peers questioning your goals, MBA ROI, or your own self-doubt underestimating your adaptability. You simply need to stay focused on your career outcomes, while enjoying the process of getting there eventually. What’s an unexpected career pivot you’ve made or seen? Krishank Parekh | LinkedIn

I've put together some data to help job seekers understand what the labor market (job market) looks like at the end of 2024 to help you plan for a smarter job search in 2024. YOUR CAREER MANTRA FOR 2025 💥 Take Control: Replace assumptions with data and focus on what you can control! The future of work is fast, complex, and evolving. Staying informed, adaptable, and focused on essential skills will set you apart in 2025. Executives are prioritizing key areas to stay ahead. 5 Key Changes Shaping 2025 (Source: LinkedIn Work Change Snapshot 2024) 1️⃣ AI technologies and tools 2️⃣ Economic uncertainty and geopolitical shifts 3️⃣ Upskilling and reskilling 4️⃣ Remote and hybrid work models 5️⃣ Multi-generational workplaces THE PACE OF CHANGE ◼ 70% of executives believe change is accelerating. ◼ 64% of professionals feel overwhelmed by how quickly jobs are evolving. ◼ 50% of all job-related skills will change in the next 5 years. Example: 68% of roles in LinkedIn’s “2024 Jobs on the Rise” didn’t exist 20 years ago! (Source: LinkedIn Work Change Snapshot 2024) 63% OF RECRUITERS USING AI (Source: Employ Recruiter Nation Report 2024) AI is transforming talent acquisition by improving: - Candidate matching - Intelligent sourcing - Automated communication via chatbots - Tailored job recommendations This helps recruiters focus on meaningful conversations while job seekers benefit from a smoother hiring process. However, challenges remain: 🔹 63% of recruiters report too many unqualified applicants (Source: iHire, 2024). 🔹 Job applications grew by 31% in early 2024 (Source: 2024 Workday Global Workforce Report) INCREASE IN SKILLS-BASED HIRING ✳ 87% of U.S. companies now embrace skills-based hiring—up from 71% in 2023 (Source: TestGorilla, 2024). Skills-based hiring is a recruitment approach that focuses on evaluating candidates based on their skills, rather than on their education or past work experience SOFT, HUMAN, ESSENTIAL SKILLS With the boom of AI tools used during the application process, emotional intelligence and soft skills remain critical: 92% of executives agree these skills are more important than ever (Source: LinkedIn Talent Blog, 2024). JOB SCAMS RISING With AI and remote work, scams are targeting both job seekers and recruiters: ❌ 20,000 task scams reported in 2024 vs. 5,000 in 2023 targeting job seekers (Source: FTC Spotlight). REMOTE vs In OFFICE WORK Employers are increasing in-office requirements: 🔸 34% of roles are now fully in-office (up 17% since 2023). 🔸 Hybrid work remains stable at 57% 🔸 Only 9% of roles are fully remote (down from 27% in 2023). (Source: Employ Recruiter Nation Report, 2024). The key to your career success is to stay informed, keep your skills fresh and tap into the power of human connections and conversations.

I helped 1,000+ people get data jobs.  Here's what no one says about 2025. Everyone fights for entry-level roles. But companies can't fill jobs that actually pay. Here’s 5 hard truths about 2025: 1. "Just SQL and Excel" jobs? Gone. AI took them. 2. Entry-level is packed. 500+ people apply per job. 3. Companies want more skills. One-trick analysts get replaced. 4. Certificates don't matter without real business sense. 5. AI is eating basic reporting jobs fast. But here's the good news. 5 chances hiding in plain sight: 1. Full Stack Analysts make $120k+. Companies need more. 2. Industry jobs pay more. Healthcare data. Supply chain. Sports. 3. Your past job helps. Teachers, nurses, drivers—you solve problems already. 4. Mid-level jobs sit empty. Everyone's stuck at the bottom. 5. Business-smart analysts can't be replaced. They make money, not just reports. 𝗜'𝘃𝗲 𝘀𝗲𝗲𝗻 𝗶𝘁 𝗵𝗮𝗽𝗽𝗲𝗻: The difference between those who make it now and those who don’t?  They stopped doing what everyone else does. They built skills for tomorrow. Not yesterday. Companies don't need more dashboard makers. They need people who: → Turn data into decisions → Automate boring work → Speak business, not just code The bar is higher now. That means the pay is too. What skill are you building for 2025?

Hey Data Aspirant, Let’s not sugarcoat it The data job market in 2025 is competitive. But it’s not dead. It’s evolving. Here’s what I’m seeing on the ground: 1. Just learning Python or SQL isn’t enough anymore. Companies are hiring analysts who can tell business stories with data - not just code dashboards. 2. AI won’t replace data analysts - but it will replace average ones. If you’re not learning how to use AI tools (like ChatGPT, Tableau Pulse, or Power BI Copilot), you're falling behind. 3. Business context > tools. Hiring managers want analysts who understand why numbers move - not just how to make them look pretty. So what do you do if you're confused right now? You don’t quit. You pivot your approach: Learn how to solve real business problems with data. Start building “dirty” projects - the kind that solve actual pain points, not Kaggle-perfect ones. Build visibility: LinkedIn isn’t just for showing wins. It's for sharing your thinking process. The analyst who thinks like a business owner will never go jobless. This is the 2025 mindset shift no one teaches in bootcamps - but it’s the one that gets interviews. So if you’re still stuck, confused, or questioning yourself… Stay. Learn the rules of the new game. And start playing it better. Because data isn’t going anywhere. But only the sharp, curious, and visible ones are moving forward. Make noise. Make impact. And make yourself undeniable. - Priyanka SG Forever learning. Forever curious. Data Analyst Mentorship : https://lnkd.in/gasgBQ6k

SQL and Python are great, but they won’t guarantee your success in 2025. Here’s what will. 👇 The data job market is evolving faster than ever. While technical skills are still important, they are no longer enough to stand out. Employers want candidates who can bridge the gap between data and decisions. Here are 3 underrated skills that will help you shine in 2025: 🔹 Data Storytelling Being able to turn complex data into actionable insights is priceless. Use tools like PowerPoint or Tableau to create compelling visuals that resonate with stakeholders. Remember: It’s not about the numbers; it’s about the story they tell. 🔹 Business Acumen Understanding your industry is a superpower. Learn how your role contributes to business goals—profitability, customer retention, or market expansion. Pro tip: Study company case studies or dive into industry blogs. 🔹 Stakeholder Management Collaborating with non-technical teams is often overlooked but essential. Build the habit of asking, “What problem are we trying to solve?” Effective communication is the key to driving impact. 💡 Resources to Get Started: 📍 For storytelling: Check out Storytelling with Data by Cole Nussbaumer Knaflic. 📍 For business acumen: Take free courses on platforms like Coursera or edX. 📍 For stakeholder management: Practice by presenting insights to friends or mentors for feedback. Your technical skills are your foundation. These underrated skills will set you apart 💯 What other skills do you think will be game-changers in 2025? Let’s discuss! #DataCareers #DataStorytelling #CareerAdvice #DataScience #AnalyticsJobs

One of the most overlooked ways to break into data is by pivoting within your current company. Last month, my client successfully made this transition—from a grocery store manager to a Data Analyst. Are they exactly where they want to be as a data scientist? Not yet. But they’re a whole lot closer. And that’s okay—breaking into data often requires humility. You might not land your dream role right away, but every step forward gets you closer. Being humble and recognizing the value of incremental progress is key to long-term success. But why is this approach so effective? Because pivoting within your company is a win-win: - Trust: Your current company already trusts you. - Cost Efficiency: Hiring and training new employees is costly for the company, and job searching is costly for you. - Simplicity: No need to send 100s of applications, tweak your resume endlessly, or struggle through interviews. Leverage the relationships you already have. So, how can you make this pivot effectively? Here are 7 action steps to help you pivot within your company: 1) Ignore the Job Title, Focus on Skills Identify the skills and key responsibilities of the role you want. These can be leveraged later if you choose to switch companies. 2) Ask Your Manager to Build a Plan Propose a plan that shows how your new role will help the company grow. Suggest dedicating a fraction of your hours to learning and building those skills. 3) Automate and Innovate Look for ways to automate your current job or optimize processes. This can demonstrate your value in a data role. 4) Internal Networking Talk to other departments, explore internal positions, and make connections. Sometimes, a simple conversation can open doors to new opportunities. 5) Volunteer and Take Initiative Offer to take on data-related tasks or projects, even if they’re outside your current role. It’s easier to ask for forgiveness than permission. 6) Review and Analyze Current Data Use the data available in your current role to drive improvements. This shows initiative and builds your data skills. Every company uses data, so find it. 7) Look for Internal Positions Regularly check for internal openings that align with your goals. Applying within your company can often be a faster route to a new role. Remember: The Easiest Change is Optimizing Your Current Situation Before you start looking externally, consider how you can leverage your current position to move closer to your dream data role. It might be simpler than you think. Have you successfully pivoted within your company or are you planning to? Let’s discuss your experience and ideas in the comments! --------- ➕ Follow Jaret André for more actionable tips on breaking into data and accelerating your career. ♻ Repost this if you found it helpful.

As we head into 2025, several trends are emerging that will shape the job market in significant ways: 1. Salary Transparency - Candidates are pushing for clarity, and regulations are likely to expand. Expect more job postings with upfront salary ranges, helping candidates and employers set expectations from the start. 2. Demand for Soft Skills & Upskilling - Technical skills have a short shelf life, making adaptability and communication more valuable than ever. Employers are investing in upskilling to help employees grow in their roles, emphasizing soft skills alongside continuous learning. 3. Flexibility in Work Arrangements - Companies are often hesitant to fully commit to remote work due to collaboration concerns, although candidates are still drawn to remote flexibility. LinkedIn reports that while only 10% of jobs are listed as remote, they receive nearly half of all applications. 4. AI’s Expanding Role & AI Literacy - AI will continue to handle routine tasks, creating new tech-focused jobs while making AI literacy valuable across all roles. Candidates who are comfortable with these tools will stand out in 2025. 5. Job Movement & Purpose-Driven Work - Job seekers, especially Millennials and Gen Z, are looking for roles that align with their values and offer work-life balance. The emphasis is shifting toward meaningful work over long-term loyalty. 6. Emphasis on DEI & Mental Health - Diversity, equity, and inclusion (DEI) initiatives are increasingly prioritized by candidates, as is mental health support. Companies that value these aspects are more likely to attract and retain talent. These trends reflect a job market that’s evolving to meet candidates’ expectations for transparency, flexibility, and purpose. Ready to navigate the changes? #JobMarketTrends #2025Hiring #Recruitment