Now / 2026
Farida Sakr
Partner Data Scientist @ Meta
I use data to understand what happened, what we should do about it, and increasingly, whether what we did actually created value.
I work in B2B partner data science, advising telecom partners across the US and LATAM. I work at the point where analysis becomes a decision: in practice, that means translating between executives and external partners on one side and product, engineering, analytics, marketing and Data / ML teams on the other.
Current question
Did the intervention actually create value?
Scope
My datasets got bigger. So did the map.
This traces where the work reaches, not where I've lived: it started with Canadian telecom network problems and grew into a B2B advisory role spanning the US and a LATAM region, not country-by-country coverage.
Canada
Bell · predictive modelling, network analytics, data products
United States
Meta, current B2B scope · partner data science, executive advisory
LATAM (regional)
Meta, current B2B scope · market & network analytics, not claimed country-by-country
Cross-functional
How my work becomes a decision
My role repeatedly sits between the people who need a decision made, the analytical work that answers it, and the decision itself.
Who I work with
- External partners
- Product + Engineering
- Analytics + Marketing + Data/ML
- Leadership + executives
What the work is
- Build + Automate
- Translate + Advise
- Analyze + Model
- Measure
What changes
- Network + operational decisions
- Partner + market strategy
- Commercial value
- Product / customer experience
Flows reflect relationships across a curated set of career highlights, not estimates of time or importance.
Full detail of the flow, since the diagram above is decorative: who connects to what kind of work, and what kind of work connects to what changes.
- External partners connects to Build + Automate
- External partners connects to Translate + Advise
- External partners connects to Analyze + Model
- Product + Engineering connects to Build + Automate
- Product + Engineering connects to Analyze + Model
- Analytics + Marketing + Data/ML connects to Build + Automate
- Leadership + executives connects to Translate + Advise
- Leadership + executives connects to Measure
- Build + Automate connects to Network + operational decisions
- Build + Automate connects to Partner + market strategy
- Build + Automate connects to Commercial value
- Build + Automate connects to Product / customer experience
- Translate + Advise connects to Network + operational decisions
- Translate + Advise connects to Partner + market strategy
- Analyze + Model connects to Network + operational decisions
- Analyze + Model connects to Partner + market strategy
- Measure connects to Commercial value
Selected impact
Some numbers I didn't have to simulate.
$2M
cost savings from replacing third-party crowdsourced data with an internal solution
40+
users of Bell's Data-as-a-Service dashboard
28%
reduction in congestion-related network impact
15+ hrs
saved monthly by the AI-powered partner-health monitoring workflow
100+ hrs
of manual reporting eliminated per event cycle through the FIFA World Cup reporting pipeline
25
analysts supported through GenAI upskilling
Reverse chronology
How I got here
Influence
Partner Data ScientistMeta
2025 – Present
- Advise senior B2B executives at major US and LATAM telecom carriers, translating consumer and network analytics into recommendations they act on.
- Built analytical and AI systems, including partner-health monitoring, a Hispanic-market segmentation model, and a global FIFA World Cup reporting pipeline.
- Led GenAI upskilling and evaluation frameworks across the analyst team, working extensively across internal teams and external partners.
Build
Data ScientistBell
2023 – 2025
- Led a cross-company initiative with an external partner to build an in-house crowdsourced data solution, and owned the analytical system it became.
- Designed a Data-as-a-Service dashboard adopted across the business, and led the data-integrity work behind it.
- Partnered externally with Apple's team on network congestion mitigation and led a strategic partnership with Meta, while regularly translating analysis into recommendations for executives, product and engineering.
Predict
Data Science InternBell
During undergraduate studies
- Built a Monte Carlo simulation model in R to forecast network performance against competitors.
- Automated geolocation mapping in PyQGIS to speed up identification of underperforming network areas.
Statistics
Undergraduate degree in StatisticsFoundation
Completed 2023
Turns out the statistics degree was useful.
Career EDA
A tiny EDA of one career
n = one career. Interpret accordingly.
Apparently I kept moving northeast.
Qualitative placement, not measured data (not a secretly validated psychometric instrument).
Projects listed below in roughly the order they moved from technical, individual work toward decision-proximate, cross-functional ownership.
Expanding scope
The questions got harder
What is likely to happen?
Monte Carlo · Predictive modelling · Model validation
Why is it happening?
Network analytics · Geospatial analysis · Segmentation
What should we do?
B2B strategy · Executive recommendations · Partner decisions · AI-enabled workflows
Did what we did actually create value?
Causal inference · Value measurement · Incremental commercial outcomes
What I'm thinking about now
I'm currently focused on value measurement: moving beyond operational improvements to quantify whether interventions causally influence customer behavior and commercial outcomes. Every link between an intervention and commercial value is a separate empirical claim.
What I'm looking for
Senior data scientist roles with real ownership: framing the question, doing the analysis or model, communicating it, and staying on the hook through the decision and the measurement of whether it worked. Most of my experience is B2B and cross-functional, and that's shaped what I think ownership actually means.