Data Science Career Guide

Building Data Pipelines has levels to it: - level 0 Understand the basic flow: Extract → Transform → Load (ETL) or ELT This is the foundation. - Extract: Pull data from sources (APIs, DBs, files) - Transform: Clean, filter, join, or enrich the data - Load: Store into a warehouse or lake for analysis You’re not a data engineer until you’ve scheduled a job to pull CSVs off an SFTP server at 3AM! level 1 Master the tools: - Airflow for orchestration - dbt for transformations - Spark or PySpark for big data - Snowflake, BigQuery, Redshift for warehouses - Kafka or Kinesis for streaming Understand when to batch vs stream. Most companies think they need real-time data. They usually don’t. level 2 Handle complexity with modular design: - DAGs should be atomic, idempotent, and parameterized - Use task dependencies and sensors wisely - Break transformations into layers (staging → clean → marts) - Design for failure recovery. If a step fails, how do you re-run it? From scratch or just that part? Learn how to backfill without breaking the world. level 3 Data quality and observability: - Add tests for nulls, duplicates, and business logic - Use tools like Great Expectations, Monte Carlo, or built-in dbt tests - Track lineage so you know what downstream will break if upstream changes Know the difference between: - a late-arriving dimension - a broken SCD2 - and a pipeline silently dropping rows At this level, you understand that reliability > cleverness. level 4 Build for scale and maintainability: - Version control your pipeline configs - Use feature flags to toggle behavior in prod - Push vs pull architecture - Decouple compute and storage (e.g. Iceberg and Delta Lake) - Data mesh, data contracts, streaming joins, and CDC are words you throw around because you know how and when to use them. What else belongs in the journey to mastering data pipelines?

It took me 6 years to land my first Data Science job. Here's how you can do it in (much) less time 👇 1️⃣ 𝗣𝗶𝗰𝗸 𝗼𝗻𝗲 𝗰𝗼𝗱𝗶𝗻𝗴 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 — 𝗮𝗻𝗱 𝘀𝘁𝗶𝗰𝗸 𝘁𝗼 𝗶𝘁. I learned SQL and Python at the same time... ... thinking that it would make me a better Data Scientist. But I was wrong. Learning two languages at once was counterproductive. I ended up being at both languages & mastering none. 𝙇𝙚𝙖𝙧𝙣 𝙛𝙧𝙤𝙢 𝙢𝙮 𝙢𝙞𝙨𝙩𝙖𝙠𝙚: Master one language before moving onto the next. I recommend SQL, as it is most commonly required. ——— How do you know if you've mastered SQL? You can ✔ Do multi-level queries with CTE and window functions ✔ Use advanced JOINs, like cartesian joins or self-joins ✔ Read error messages and debug your queries ✔ Write complex but optimized queries ✔ Design and build ETL pipelines ——— 2️⃣ 𝗟𝗲𝗮𝗿𝗻 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝘀 𝗮𝗻𝗱 𝗵𝗼𝘄 𝘁𝗼 𝗮𝗽𝗽𝗹𝘆 𝗶𝘁 As a Data Scientist, you 𝘯𝘦𝘦𝘥 to know Statistics. Don't skip the foundations! Start with the basics: ↳ Descriptive Statistics ↳ Probability + Bayes' Theorem ↳ Distributions (e.g. Binomial, Normal etc) Then move to Intermediate topics like ↳ Inferential Statistics ↳ Time series modeling ↳ Machine Learning models But you likely won't need advanced topics like 𝙭 Deep Learning 𝙭 Computer Vision 𝙭 Large Language Models 3️⃣ 𝗕𝘂𝗶𝗹𝗱 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 & 𝗯𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝘀𝗲𝗻𝘀𝗲 For me, this was the hardest skill to build. Because it was so different from coding skills. The most important skills for a Data Scientist are: ↳ Understand how data informs business decisions ↳ Communicate insights in a convincing way ↳ Learn to ask the right questions 𝙇𝙚𝙖𝙧𝙣 𝙛𝙧𝙤𝙢 𝙢𝙮 𝙚𝙭𝙥𝙚𝙧𝙞𝙚𝙣𝙘𝙚: Studying for Product Manager interviews really helped. I love the book Cracking the Product Manager Interview. I read this book t𝘸𝘪𝘤𝘦 before landing my first job. 𝘗𝘚: 𝘞𝘩𝘢𝘵 𝘦𝘭𝘴𝘦 𝘥𝘪𝘥 𝘐 𝘮𝘪𝘴𝘴 𝘢𝘣𝘰𝘶𝘵 𝘣𝘳𝘦𝘢𝘬𝘪𝘯𝘨 𝘪𝘯𝘵𝘰 𝘋𝘢𝘵𝘢 𝘚𝘤𝘪𝘦𝘯𝘤𝘦? Repost ♻️ if you found this useful.

Out-of-stock products are a major frustration in online grocery shopping. When customers order their weekly essentials only to find that an item isn’t available, the quality of the suggested replacement can make or break their experience. In a recent tech blog, data scientists at Instacart shared how they tackled this challenge with a customized machine learning system built on two complementary models. - The first model leverages product category information to understand general similarity relationships across the catalog. This helps address the cold-start problem — when new or niche items lack sufficient engagement data to capture customer preferences. - The second model, known as the engagement model, learns directly from user behavior — such as which replacements were accepted or rejected. This enables the system to “remember” customer preferences for popular products and more accurately reflect how people perceive product similarity. During development, the team discovered an interesting bias: the model tended to favor well-known national brands that appear across multiple retailers, rather than local store brands. To fix this, they made the system retailer-aware by incorporating retailer IDs into its schema. This small but powerful adjustment led to more relevant and balanced recommendations — better aligned with customer expectations and price preferences. This project is a good example of how customized machine learning architectures can address real-world business challenges, and a nice read for anyone interested in applied machine learning. #DataScience #MachineLearning #Recommendation #Engagement #Customization #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gFYvfB8V    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gXA9N4dZ

95% of retailers reported using AI in their business. Adoption is widespread! But we also found that just 5% reported seeing clear scalable ROI on their investment according to our latest research in partnership with Voyado. It’s a stark statistic! We found the landscape of AI sophistication across the retail sector is very uneven. We mapped out the transitional journey, and most businesses businesses all into one of four stages. ➡️ Phase 1: Exploration, where retailers are testing AI through pilots and proof-of-concepts. ➡️ Phase 2: Pilot scaling, where AI is used in selected functions, but not yet embedded across workflows. ➡️ Phase 3: Operational, where AI is integrated into several core marketing and e-commerce processes. ➡️ Phase 4: Embedded strategy, where AI informs decision-making at a strategic level and is woven into planning, execution and optimization across the business. What this tells me is that the ambition is high, but many businesses are faltering because structure, data and culture haven’t caught up. Part of the reason for this is because as organisations scale, complexity increases faster than integration capability. They end up with fragmented systems and more complex governance layers, which slows down decision cycles. To this end, AI maturity rarely progresses in a straight line. As we've heard many times before, data is the key differentiator. But what makes the real difference is the structural integration of customer, product, and commercial data - all together. These three areas will be critical in determining how far AI can act autonomously and how confidently it can optimise decisions across the business. In the industry, we have spoken about personalisation for a very long time. But real personalisation sits at the intersection of data, decisions, and execution. For this to work, it requires unified customer signals, connected workflows across channels and organisational confidence in automated optimisation. This is not easy. Our research shows that lack of internal skills as the primary barrier to advancing AI - cited by 58% of retailers. Most retailers have access to advanced AI tools through platforms or vendors. But few have the in-house expertise to deploy, govern and optimize them at scale. This limits their ability to tune and refine models, and creates uncertainty as to how to measure AI-driven performance. What separates advanced retailers is how far that integration extends. We found that in more advanced organisations, AI is integrated earlier in the decision chain. It informs planning, prioritisation, and commercial trade-offs across functions. This can include: ➡️ Influencing budget allocation across channels ➡️ Informing pricing and promotional strategy ➡️ Shaping inventory and margin trade-offs Things are moving at warp speed at the moment. Keep up by downloading our latest research here: https://lnkd.in/eqJibUb4

🧱 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 ≠ 𝗝𝘂𝘀𝘁 𝗠𝗼𝘃𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 That myth limits your growth before it begins. It is the foundation layer. Without it, AI/ML teams can’t scale. Data engineering isn’t about pipelines alone— It’s about building platforms that power decisions and collaborating across teams to make data truly valuable. 🚫 𝗧𝗵𝗲 𝗠𝗶𝘀𝗰𝗼𝗻𝗰𝗲𝗽𝘁𝗶𝗼𝗻 Most people think data engineering ends at ETL. But in reality, we architect the systems that make data usable, trustworthy, and scalable—in partnership with analysts, product teams, and engineers. 🤝 𝗪𝗵𝘆 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 Modern data engineering is not a solo act. You’re not just building pipelines—you’re enabling:  • Analysts to explore and visualize data  • Product teams to make informed decisions  • Engineers to integrate data into applications  • Governance teams to ensure compliance and trust  • Without collaboration, even the best pipelines go unused. 🧩 Two Evolution Paths for Data Engineers - 📊 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀-𝗙𝗼𝗰𝘂𝘀𝗲𝗱 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 - The Foundation Builders They ensure business teams have clean, well-modeled, and governed data to work with. - What they do:  • Build batch pipelines and data marts  • Design semantic layers and data contracts  • Partner with analysts and BI teams - Core skills:  • SQL & dimensional modeling  • Apache Spark, Airflow, dbt  • Data warehouse tuning (Snowflake, BigQuery)  • Data quality frameworks 🏗️ 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺-𝗢𝗿𝗶𝗲𝗻𝘁𝗲𝗱 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝘀 - The Infrastructure Enablers They build the systems that scale data across teams, products, and use cases. - What they do: • Architect real-time and event-driven pipelines  • Build self-service data platforms  • Collaborate with infra, security, and product teams - Core skills:  • Stream processing (Kafka, Flink)  • Data lakehouse architecture (Delta, Iceberg)  • API design & metadata management  • Infra-as-Code (Terraform, CDK) 🎯 𝗧𝗵𝗲 𝗚𝗼𝗮𝗹: 𝗕𝗲 𝗧-𝗦𝗵𝗮𝗽𝗲𝗱  • Deep in your core path (analytics or platform)  • Broad across the data lifecycle  • Collaborative across teams and domains ✅ 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸  • Analytics engineers must understand how data is consumed  • Platform engineers must understand how data is used  • Both must design for collaboration, scale, and change 🧭 𝗖𝗵𝗼𝗼𝘀𝗲 𝗬𝗼𝘂𝗿 𝗘𝘃𝗼𝗹𝘂𝘁𝗶𝗼𝗻 📊 Love modeling and enabling insights? → Analytics Foundations 🏗️ Love building systems and infra? → Platform Engineering But remember: Data engineering is a team sport. Start deep in your strength. Grow into the ecosystem. Collaborate to scale your impact. 💬 Your Turn Which path are you on: Analytics-Focused or Platform-Oriented? Stay tuned with me(Pooja Jain) for more on #Data #Engineering!

Data engineering isn't Apache Spark. Data engineering isn't Apache Kafka. Data engineering isn't Apache Airflow. Data engineering isn't Snowflake. Data engineering isn't Apache Hadoop. Data engineering isn't Google BigQuery. Data engineering isn't Apache Cassandra. Data engineering isn't Databricks. Data engineering isn't Apache Flink. Data engineering isn't Amazon Redshift. Data engineering isn't just code. It's about understanding data flow. It's database design and optimization. It's data modeling and schema evolution. It's ensuring data quality and consistency. It's building scalable and resilient systems. It's optimizing query performance. It's designing ETL and ELT processes. It's managing data lineage and governance. It's balancing consistency, availability, and partition tolerance. It's turning raw data into valuable insights. Tools and platforms are enablers. The core of data engineering is architecture. Without solid principles, the pipelines are fragile. Tools come and vanish, but principles endure. Today's cutting-edge platform is tomorrow's legacy system. Master the fundamentals, and you can adapt to any tool. Note: Data engineering isn't about fancy tools—it's about how those tools are leveraged to create robust, scalable, and efficient data ecosystems. #DataEngineering #BigData #DataArchitecture #ETL #DataPipelines

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?

Data analysts, ever wondered what roles in the data field you could grow into? Here are some exciting paths and how to prepare for them: 1. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁: Dive deeper into advanced analytics, machine learning, and statistical modeling. Start by learning Python, and machine learning via online courses and YouTube, and apply these skills to your current projects.     2. 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿: Focus on building and maintaining the infrastructure that allows for data collection, storage, and analysis. Learn about ETL processes, data warehousing, and Big Data processing with Python and Spark. Try to get involved in the development of the pipelines that provide the data for your analysis.     3. 𝗕𝘂𝘀𝗶𝗻𝗲𝘀𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝘁: Bridge the gap between data and business stakeholders. Master BI tools like Tableau and Power BI, and practice creating dashboards that drive decision-making. Work on your soft skills and actively engage with your stakeholders.     4. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿: Specialize in designing and deploying machine learning models. Enhance your software engineering skills and learn about ML algorithms. Start by experimenting with model deployment in small projects.     5. 𝗗𝗮𝘁𝗮 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁: Design the overall data strategy and architecture for an organization. Study database design, cloud computing, and data governance. Contribute to the design of data management systems in your current job.     6. 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗿: Combine your data expertise with product management skills to oversee the development of data-driven products. Learn about product lifecycle management and customer insights. Collaborate closely with product teams to understand their processes.     7. 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝗮𝗹 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿: Focus on the intersection of data analysis and data engineering. Develop skills in advanced SQL, Python, data modeling, and performance optimization. Start by improving the efficiency and scalability of your current data workflows. Continuously expand your skill set and stay curious about the work of the other professions. Seek out mentorship and network with professionals in your desired field to gain insights and guidance. By exploring these career paths and proactively preparing for the transition, you can leverage your current role to get ready to explore interesting new careers in the data field. Which data role are you most interested in exploring? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanalytics #datascience #datacareers #careergrowth #businessanalytics

#AI is quietly transforming healthcare for Early Disease Detection. Imagine catching cancer, diabetes, or even rare diseases before symptoms appear! That’s not science fiction. AI-powered tools are already helping doctors spot early warning signs in everything from #breastcancer to #Alzheimer’s, often faster and more accurately than traditional methods. Take #cancer detection: Google’s AI model for #mammograms reduced false positives by 5.7% and false negatives by 9.4% compared to human radiologists. In pancreatic cancer, Harvard Medical School researchers showed AI could predict who’s at highest risk up to three years before diagnosis (https://lnkd.in/dWzFbG_D)) For #rarediseases, AI platforms like Face2Gene and FABRIC GEM INC. are cutting years off the diagnostic journey. (https://lnkd.in/dQyHAU43); (https://lnkd.in/duEWYHHW). In chronic conditions like #diabetes and heart disease, AI-driven wearables and apps are helping patients and clinicians manage care in real time. (https://lnkd.in/dDbjr4UM). The research is booming: a recent review found a surge in AI studies on non-communicable diseases, with top institutions like Harvard and the Ministry of Education of China leading the way. (https://lnkd.in/dhYYxEVy)). Policy is catching up too! The US HHS released its 2025 Strategic Plan for AI in Healthcare, outlining regulatory priorities for safety, transparency, and compliance. (https://lnkd.in/d3--iAmG). But hurdles remain: AI models need diverse, high-quality data to avoid bias and ensure real-world accuracy. Regulatory standards are evolving, and healthcare leaders must balance innovation with patient safety and privacy. The solution? Collaborate early with clinicians, data scientists, and regulators; invest in robust trials; and prioritize transparency and equity. Healthcare leaders: AI isn’t just the future... it’s here now. If you want to explore how to bring these breakthroughs to your organization, connect with me. Let’s shape the next wave of healthcare together.

Want to grow fast in data engineering? Start thinking in first principles. I get this question a lot: “What tools should I learn to get a data engineering job?” Here’s the truth: Tools are temporary. Principles are permanent. One company might be using Spark. Another might use an internal framework. Next year, they might switch to something entirely new. In this ever-evolving landscape, tools change. But what doesn’t change is the why and how behind them. Instead of chasing tools, ask deeper questions: • How is data distributed for processing? • What makes a good partitioning strategy? • How do you avoid data skew? • What affects node health and compute performance? • How can I reduce storage and compute costs? • How do I build for scale, fault tolerance, and reliability? These are first principles. Understand these well, and you can adapt to any tool—Spark, Flink, Snowflake, or whatever comes next. Tools are wrappers. Master the fundamentals, and tools will never limit you. #DataEngineering #FirstPrinciples #CareerAdvice #DistributedComputing #LearningMindset #BigData #TechGrowth