Data Analytics (Intern -> Fulltime)

Round 1: Online assessment (Python & SQL coding test) → Round 2: Technical interview (data analytics concepts, case study discussion) → Round 3: HR interview (cultural fit, motivation, salary discussion)

LinkedIn is renowned for an open, inclusive culture that encourages continuous learning and collaboration. Employees benefit from clear growth pathways, regular feedback, and a strong emphasis on work‑life balance, making it an attractive platform for fresh talent.

LinkedIn‑hosted interviews typically start with a timed online coding test focusing on Python and SQL, followed by a technical interview where candidates discuss past projects, data‑cleaning approaches and statistical reasoning. The final HR round assesses cultural fit, communication skills and career aspirations. Overall difficulty is moderate, with emphasis on clear problem‑solving methodology.

Final‑year undergraduate or postgraduate students (2025 batch) in Statistics, Economics, Mathematics, Engineering or related fields. Minimum 60% aggregate (or CGPA 6.0/10). No active backlogs at the time of joining. Candidates with prior analytics or data‑science internship experience are preferred.

Affluense.ai is an AI‑powered intelligence platform that serves India’s wealth‑management and BFSI (Banking, Financial Services and Insurance) sectors. By aggregating millions of public data points—company filings, director networks, funding rounds, news articles and more—the platform builds a detailed picture of high‑net‑worth individuals and private companies. The startup is seed‑funded, rapidly scaling, and already counts several enterprise customers among its users. Its mission is to turn noisy, unstructured data into actionable insights that help financial advisors and wealth managers identify prospects, understand wealth creation pathways, and anticipate client needs. The Data Analytics internship is a three‑month, hands‑on program that can convert into a full‑time role based on performance. Interns will work directly with the founders—both seasoned data scientists with over 14 years of experience—on real‑world problems from day one. The focus is purely analytics: exploring massive, messy datasets, extracting hidden signals, and translating findings into product‑ready insights. This is not a chatbot or LLM‑centric role; instead, it emphasizes statistical rigor, data cleaning, and business‑oriented storytelling. Key responsibilities include: 1. Exploring large, unstructured datasets such as company filings, director networks, funding histories and news feeds to uncover wealth‑related signals. 2. Estimating individual wealth using indirect indicators like ownership stakes, directorships and business performance, often without ground‑truth labels. 3. Identifying private companies with high IPO potential by detecting early‑stage growth patterns in their filings and funding trajectories. 4. Cleaning, joining, and validating data from multiple sources, ensuring accuracy before it reaches customers. 5. Building and maintaining robust data pipelines using Python (pandas) and SQL. 6. Conducting statistical analyses to understand distributions, correlation versus causation, and sampling bias. 7. Visualising findings in clear, concise dashboards or presentations for the founding team. 8. Documenting methodology and creating reproducible notebooks for future reference. 9. Collaborating with product and engineering teams to translate analytical insights into product features. 10. Continuously learning and applying basic machine‑learning techniques (regression, classification, clustering) when beneficial. The tech stack revolves around Python (pandas, NumPy), SQL, Excel for quick ad‑hoc analysis, and version‑controlled Jupyter notebooks. Familiarity with data‑visualisation libraries (Matplotlib, Seaborn) and basic Git workflows is advantageous. While advanced ML is not mandatory, exposure to scikit‑learn will help accelerate learning. Growth path: Successful interns receive a full‑time offer, joining a fast‑growing analytics team. Over time, they can progress to Senior Data Analyst, Data Scientist, or Product Analytics Lead, with opportunities to lead cross‑functional projects and shape the company’s data strategy. Why join Affluense? You will gain exposure to high‑impact financial data, work directly with founders, and own end‑to‑end analytical projects that ship to enterprise customers. The role offers a steep learning curve, mentorship from industry veterans, and a clear pathway to a full‑time data career in a niche, high‑growth domain.