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Open nowPosted 17 days ago

Commercial Intelligence & Business Analyst - Oil & Gas

Workable (global search)108,016 open roles

Where
Egypt
Work mode
Remote
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Your applicationOpen nowCommercial Intelligence & Business Analyst - Oil & GasWorkable (global search) · Egypt

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  2. CV RocketCV written for this posting.

  3. 25 readersRecruiter, hiring manager, skeptic. Round after round.

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The clock on this job

Early applications get read.

7.9% of postings close within 7 days. Measured by our own scanner across the market. Workable (global search) postings stay open a median of 7 days.

Share of postings closed within
  1. 1.6%1 day
  2. 3.6%3 days
  3. 7.9%7 days
  4. 14.9%14 days
  5. 34.0%30 days
This job: posted 17 days ago

Workable (global search) median: 7 days open

The posting

Key partners

Commercial Operations Analyst; Technical Data & Documentation / Marketing Communications lead; sales; product line management

Role purpose

The analytical engine of the commercial team. The role owns the market, competitive, and business intelligence that informs strategy, pricing, and product decisions, together with the CRM and commercial data the function runs on.

It turns data into clear insight and reliable reporting, and operates the commercial intelligence AI agents day to day under the director's governance. The role is analytical rather than creative — brand, content, and collateral sit elsewhere in the team — so the focus is depth in data, analysis, and decision support.

Scope of ownership

• Market, competitive, and demand intelligence and the reporting built on it.

• CRM data integrity, pipeline hygiene, and standard commercial reporting.

• Commercial and customer analysis — segmentation, win/loss, profitability, and quote analytics.

• Day-to-day operation of the market-signal and account-intelligence agent, plus analytical support to the pricing and tender agents.

• Data quality across the commercial systems.

Key responsibilities

Market & competitive intelligence

• Maintain the market, competitive, and demand intelligence that informs commercial strategy, pricing, and product decisions.

• Provide market sizing, competitive positioning, and account intelligence to support major tender development.

• Track competitor activity, pricing signals, and market shifts, and translate them into concise briefs the team can act on.

Business intelligence & reporting

• Build and maintain dashboards and standard reporting that give the wider Strategic Marketing & Technology organization a clear, current picture.

• Own win/loss reporting and surface the patterns behind wins and losses.

• Deliver ad hoc analysis to support commercial and leadership decisions.

• Data analysis, extraction and visualize to use in reporting

CRM & commercial data management

• Own CRM data integrity, pipeline hygiene, and adoption support across the commercial team.

• Manage lead and opportunity data — capture, routing, and tracking — so the pipeline is accurate and reportable.

• Maintain the commercial data foundation that feeds both reporting and the AI agents.

Requirements

Commercial & customer analysis

• Analyze customer segments, buying behavior, and account profitability to inform targeting and prioritization.

• Support pricing with quote, win-rate, discount, and cost-to-serve analysis.

• Provide the analytical backbone for pipeline reviews, forecasting inputs, and sales-effectiveness measurement.

Commercial strategy & sales effectiveness support

• Support go-to-market planning with target-account analysis and prioritization.

• Translate performance and market data into recommendations that improve win rate and quote-cycle speed.

• Partner with the director on the commercial planning rhythm — pipeline review and win/loss analysis.

AI tools operation & data quality

• Operate the market-signal and account-intelligence agent day to day, reviewing outputs for accuracy before they reach the team.

• Supply clean, current data and inputs to the pricing-intelligence and tender-drafting agents.

• Act as a front-line guardian of data quality, recognizing that the agents are only as trustworthy as the data behind them.

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