Key Responsibilities and Required Skills for Web Research Analyst

🎯 Role Definition

The Web Research Analyst is a data-driven investigator who combines web discovery techniques, automated scraping, public record retrieval, and qualitative source verification to build reliable datasets and insights. This role serves marketing, sales, product, risk, and executive teams by turning disparate online signals into prioritized, machine-ready intelligence and human-readable reports. Ideal candidates are proficient in web scraping technologies, open-source intelligence (OSINT) methods, API integrations and data hygiene practices, and can translate research outputs into business recommendations and dashboard-ready datasets.

📈 Career Progression

Typical Career Path

Entry Point From:

Research Assistant or Research Coordinator (marketing, academic or field research)
Junior Data Analyst or Data Entry Specialist with strong web research experience
Content/SEO Specialist or Digital Marketing Researcher with competitive intelligence exposure

Advancement To:

Senior Web Research Analyst
Intelligence Analyst / Competitive Intelligence Lead
Data Analyst / Research Team Lead
Product Insights Manager or Market Research Manager

Lateral Moves:

Business Development Researcher
Sales Operations / Lead Enrichment Specialist
Compliance & Risk Investigator (fraud/AML)
UX Research / Market Insights roles

Core Responsibilities

Primary Functions

Design and execute large-scale web research projects end-to-end, including defining research goals, selecting sources and methods, implementing automated and manual collection workflows, and delivering structured datasets ready for analysis or ingestion into internal systems.
Build, maintain and optimize web scraping pipelines using tools and languages such as Python, Scrapy, Selenium, BeautifulSoup, Puppeteer or managed scraping platforms, ensuring reliability, scalability and efficient handling of dynamic or JavaScript-heavy sites.
Develop and maintain API integrations with public and commercial data providers (e.g., social platforms, business registries, data marketplaces) to enrich internal datasets and automate routine enrichment tasks with robust error handling and rate-limit management.
Conduct open-source intelligence (OSINT) investigations to identify company hierarchies, ownership structures, domain registrations, social profiles and digital footprints; verify claims and uncover hidden or non-obvious connections to support investigations and due diligence.
Perform high-quality data cleaning, normalization, entity resolution and deduplication to produce accurate canonical records, using SQL, Python (pandas), regular expressions and data transformation tools; document transformation logic and assumptions for reproducibility.
Produce analyst-grade research briefs, competitive landscape summaries, lead lists and risk assessments with clear methodology, source citations, confidence levels and recommended next steps tailored to stakeholder needs (sales, product, compliance, executive).
Validate and triangulate critical data points (contact information, ownership, financial signals, IP addresses, geolocation) by cross-referencing multiple independent sources and archived web captures (Wayback Machine, cached pages) to ensure provenance and reliability.
Design and implement monitoring and alerting workflows to track brand mentions, executive moves, regulatory filings, domain changes and other events of interest using search operators, RSS feeds, scraping jobs and cloud-based schedulers.
Create reusable data extraction templates and parsers for recurring research tasks; document technical specifications, edge cases and maintenance procedures to reduce time-to-insight for subsequent requests.
Work closely with data engineering and product teams to define ingestion schemas, API specifications and data quality SLAs for research outputs; coordinate handoffs and troubleshoot production data issues.
Translate research questions into measurable data requirements and sampling strategies; prioritize research tasks based on impact, feasibility and alignment with business objectives.
Conduct manual verification and quality assurance on automated extraction results; implement sampling QA procedures and corrective feedback loops to continuously improve accuracy and reduce false positives/negatives.
Provide targeted lead enrichment and prospecting support for sales and marketing teams by compiling contact lists, firmographic and technographic profiles, outreach-ready summaries and scoring signals that feed into CRM and marketing automation systems.
Maintain a catalog of high-value open and proprietary data sources (government registries, patent databases, domain registries, industry directories), including access notes, licensing constraints and typical use cases to accelerate future research.
Apply web analytics and keyword research techniques to assess online visibility, content strategies and emerging topics within competitive sets; surface SEO and content intelligence that informs marketing and product decisions.
Support regulatory, compliance and fraud investigations by tracking suspicious domains, shell entities, social account networks and historical content; preserve forensic evidence and follow chain-of-custody best practices for sensitive cases.
Automate repetitive research tasks and reporting using scripts, scheduled jobs and low-code tools to increase throughput while maintaining data governance and traceability.
Deliver interactive dashboards, charts and visualizations (Looker, Tableau, Power BI, Google Data Studio) or CSV/JSON exports that summarize key metrics, trends and anomalies for executive briefings and operational teams.
Mentor junior researchers and cross-functional stakeholders in web research best practices, browser-based advanced search techniques, and source verification methods to raise organizational research maturity.
Stay current with legal, ethical and technical changes affecting online data collection (robots.txt, rate limits, site terms of service, privacy regulations) and adapt workflows to ensure compliance and minimize legal risk.
Provide ad-hoc research support during high-priority business initiatives (M&A due diligence, product launches, crisis response), rapidly scoping and delivering actionable intelligence under tight deadlines.
Conduct geolocation and IP attribution analysis when required, using DNS records, WHOIS, ASN lookups and reverse IP techniques to map hosting relationships and infrastructure relevant to investigations.
Implement metadata tagging, provenance fields and confidence scoring for every research record to support downstream filtering, auditing and model-training use cases.

Secondary Functions

Support ad-hoc data requests and exploratory data analysis.
Contribute to the organization's data strategy and roadmap.
Collaborate with business units to translate data needs into engineering requirements.
Participate in sprint planning and agile ceremonies within the data engineering team.
Maintain documentation of research playbooks, extraction templates, and troubleshooting guides to enable knowledge transfer and operational continuity.
Coordinate with legal and privacy teams to manage data retention, anonymization and sharing policies for research datasets.
Assist in vendor evaluation and procurement for third-party data sources and SaaS research platforms.

Required Skills & Competencies

Hard Skills (Technical)

Web scraping & automation (Python, Scrapy, Selenium, Puppeteer or equivalent).
API integration and scripting (REST APIs, JSON, OAuth, rate limit handling).
Data transformation and cleaning (pandas, SQL, regular expressions, Excel/Google Sheets advanced functions).
Open-Source Intelligence (OSINT) techniques: domain/WHOIS, reverse image search, archives, social media forensics.
Database querying and ETL basics (SQL, simple data pipelines, CSV/JSON handling).
Familiarity with cloud schedulers and job orchestration (Cron, Airflow basics or SaaS schedulers).
Data visualization and reporting (Tableau, Looker, Power BI or Google Data Studio).
Knowledge of browser devtools, network inspection and DOM analysis for extracting dynamic content.
Proven experience with data quality tools and approaches: deduplication, entity resolution, confidence scoring.
Search operators and advanced Google/Bing techniques, plus working knowledge of specialized search engines and public records portals.
Experience working with CRMs or marketing automation platforms for lead enrichment (Salesforce, HubSpot preferred).
Basic knowledge of privacy and compliance considerations (GDPR, CCPA) as they apply to data collection and storage.

Soft Skills

Strong analytical thinking and curiosity — able to design hypotheses and test them using web evidence.
Excellent written communication: distill complex research into concise, stakeholder-friendly reports.
Detail-oriented with a strong commitment to data accuracy and source provenance.
Project management and prioritization skills to manage multiple concurrent research streams.
Collaboration mindset: works effectively with engineers, product owners, sales and legal.
Adaptability to evolving data landscapes, new tools and changing legal/technical constraints.
Problem-solving under pressure for time-sensitive investigations and ad-hoc requests.
Ethical judgment and integrity when handling sensitive or regulated data.

Education & Experience

Educational Background

Minimum Education:

Bachelor's degree OR equivalent professional experience in research, analytics, computer science, information science, library science, journalism, or a related field.

Preferred Education:

Bachelor's or Master's degree in Data Science, Information Systems, Computer Science, Library & Information Science, Criminal/Forensic Studies, or Market Research.

Relevant Fields of Study:

Data Science / Analytics
Computer Science / Software Engineering
Library & Information Science / Archival Studies
Journalism / Investigative Reporting
Business / Market Research
Political Science / Criminal Justice (for intelligence-focused roles)

Experience Requirements

Typical Experience Range: 2–5 years for mid-level roles; 5+ years for senior analyst or lead roles.

Preferred:

Demonstrable track record of designing and delivering web research projects and building reusable extraction pipelines.
Experience contributing to product or sales outcomes through research-led insights (lead lists, competitive intelligence, risk assessments).
Familiarity with legal/regulatory constraints on web data collection and experience collaborating with legal/privacy teams.
Portfolio or documented examples of prior research projects (sample datasets, dashboards, write-ups) demonstrating technical and analytical capabilities.