Social Media Data Analyst

6 years

0 Lacs

Posted:3 weeks ago| Platform: Linkedin logo

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Work Mode

Remote

Job Type

Full Time

Job Description

Social Media Data Analyst

About Deeter Investments

Social Media Data Analyst

Role Description

You’ll be the point person for sourcing, cleaning, and interpreting social-media and web-native data (X/Twitter, Reddit, TikTok, YouTube, Discord/Telegram, major news, and niche forums). Your job: identify early moves, sentiment shifts, and “virality” patterns that matter for markets—and get those insights into traders’ hands fast via dashboards, alerts, and research-ready datasets.

This is a hands-on role: you’ll pull data from APIs/brokers, structure it, label it, score it, test what actually predicts price/volume, and ship lightweight tools that the team uses daily.

Key Responsibilities

Data sourcing & hygiene:

Entity & ticker extraction:

Signal & sentiment:

Quality & governance:

Collaboration:

Qualifications & Experience

·      3–6+ years in data analysis or applied analytics (content, social, growth, alt-data, or market intelligence).

Python

·      Practical NLP toolkit (regex → embeddings/classifiers); able to explain tradeoffs in simple terms.

·      Experience with social-platform APIs, third-party data brokers, or ethically compliant web ingestion.

·      Solid statistics for backtests and A/B-style validation; know how to avoid obvious pitfalls (look-ahead bias, survivorship, data leakage).

·      Communicates crisply: turns complex evidence into one-page briefs and clear “what to do” recommendations.

Nice to Have

·      Markets familiarity (tickers, earnings, filings, corporate actions) and event-study workflows.

·      Graph/“influence network” features, basic time-series modeling, or anomaly detection.

·      Experience with columnar data and fast queries (Parquet/Delta/Iceberg; DuckDB/ClickHouse/BigQuery/Snowflake).

·      Light multimodal experience (ASR/transcription, OCR for screenshots, basic image/video metadata).

Example Projects You Might Ship in Month 1–3

ticker-tagging & sentiment

virality tracker

event study

What Success Looks Like

·      Higher signal-to-noise for the desk; earlier heads-up on real catalysts; measurable lift in PnL attribution to social signals.

·      Clear, reproducible metrics: coverage %, freshness (latency), precision/recall for tagging, and backtest effect sizes with confidence intervals.

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