Context & Core Problem
Venture capital used to be a distribution game. That still matters. What changed is that distribution is now table stakes. Data, tooling, and execution transparency separate winners from followers.
Analyst-grade tools exist for two reasons:
To find signals faster.
To make decisions auditable and repeatable.
Shark Catalyst and the India FinTech dashboard prove the thesis: better information changes where capital goes and how quickly decisions happen.
Why Information Matters More Than Capital
Capital scales a company. Information changes which companies deserve capital.
Good information:
Reduces noise.
Speeds conviction.
Lowers false positives.
With better signals, you can:
Identify investor conviction pockets before they trend.
Test hypotheses quickly and reproducibly using SQL and statistics.
Tie operational outcomes directly to investment signals.
Compete on checks, you follow cycles. Compete on information, you create cycles.
What I Built and Why It Proves the Thesis
Shark Catalyst:
SQL-first analytics engine for startup pitches and investor behavior.
Validates/refutes assumptions using reproducible tests.
Outcomes: 600+ pitches analyzed, 20+ hypotheses tested, investor profiles revealing actionable patterns.
India FinTech Dashboard:
Aggregates and standardizes raw funding data across years and geographies.
Converts noisy public rows into a clean model with 9 custom DAX metrics, 11 visuals, and global slicers.
Provides a repeatable decision surface for VC scouting and sector allocation.
My Process & Playbook
A concise, repeatable information advantage playbook:
1. Ingest Clean Truth
Standardize names, dates, currencies.
Resolve missing fields.
Remove mechanical friction before analysis.
2. Make Evidence Cheap & Reproducible
Every chart links to its producing SQL.
Parameterized tests for quick verification.
3. Create Data Liquidity
Move signals from notebooks to shared views/materialized tables.
Reduce time to insight across the team.
4. Surface Execution Visibility
Connect signals to outcomes (e.g., post-investment events, user metrics).
5. Build Decision UIs
Leaders get one-sentence verdicts and confidence context.
Analysts get raw queries and caveats.
Tools & Methods
SQL and reproducible queries for all metrics.
Statistical tests (clear thresholds, effect sizes) for hypothesis validation.
Lightweight ETL in Python/Pandas for data cleaning/enrichment.
Power BI/Plotly for analyst-grade interactive views.
Materialized views and cached queries for fast answers.
Recruiter & Fund Value
These methods:
Reduce time spent arguing numbers.
Increase time spent on portfolio work.
What hiring managers want:
Pipelines that scale data from raw to actionable.
Reproducible hypothesis tests that inform deals.
Analytics turned into decision-ready products.
I deliver systems that produce consistent signals, not one-off charts. That changes how funds source, validate, and support companies.
Practical Example You Can Use Tomorrow
Pick a single hypothesis you care about.
Standardize the fields you need.
Write one SQL query to define success.
Run a simple Mann–Whitney or Chi-square test if comparative.
Put the result in a one-line verdict card and link the full query.
Repeat weekly.
This is how you turn opinion into evidence.
