I treat marketing like a product: instrumented, attributed, and built around how users actually behave.

My work sits between analytics engineering and go-to-market. I turn raw signals like customer feedback, attribution data, and content performance into systems that hold up.

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About

I'm a product and growth professional who approaches marketing like an engineering problem: instrument it, attribute it, and obsess over how real users actually behave.

At Autonomous, a B2B AI agency, I own the full marketing stack end to end. That ranges from scoping a server-side tracking architecture to recover attribution after iOS 14, to building AI content tooling that keeps brand voice consistent as output scales. I like the parts of growth most people hand off to someone else: the pipelines, the tracking, the automation underneath the content.

My MSBA capstone grew out of a gap I kept hitting in practice: B2B agencies had no scalable way to turn customer feedback into product signals. So I built one, a live, open-source sentiment intelligence tool.

Before this, I worked in product operations (Agile delivery, UX research, journey mapping) at a startup studio, and started my career in relationship banking. That ground-level path taught me what most dashboards can't: where users actually drop off, and what growth really means at the unit level.

Case Studies

Turning Customer Feedback Into Product Signals

G2 / Reddit / CSV NLP Classification Signals + Ad Copy

Problem

B2B marketing agencies aggregate customer feedback by hand: copy-pasting G2 reviews, scanning Reddit threads, eyeballing competitor mentions. There's no scalable way to track share-of-voice or turn raw sentiment into decisions a product team can act on.

Process

I built an open-source sentiment intelligence dashboard that ingests Reddit API data, G2 and Capterra reviews, and CSV uploads. Every architecture choice traded against a constraint: DistilBERT over BERT-base for roughly 5x faster inference at under 3% accuracy loss, because the dashboard had to feel real-time; GoEmotions 27-category classification over binary polarity, because in B2B feedback the difference between frustration and disappointment implies a different product action; Selenium for G2 and Capterra, since both render client-side and block simple scrapers.

Solution

The tool runs multi-model NLP classification to surface product gaps and generates LLM-drafted ad copy from the sentiment it finds. I moved inference to ONNX Runtime to drop PyTorch as a deploy dependency, and used Streamlit to iterate on the BI layer without pulling in a frontend engineer.

Impact

80% classification accuracy, 0.35s inference latency.

Learnings

The DistilBERT trade-off was right for a live dashboard, but the sub-3% accuracy gap is real. For a batch report where latency doesn't matter, I'd run the heavier model instead. The point was matching the tool to the constraint, not the benchmark.

Building Organic Presence From Zero

Solo Creator Automated Pipeline FFmpeg / ElevenLabs / HeyGen LinkedIn / IG

Problem

Autonomous had no organic presence in a crowded B2B AI market and no paid acquisition budget. Growth had to come entirely from owned content, with no team, no ad spend, and no existing audience to build on.

Process

I owned go-to-market content end to end: research, scripting, shooting, editing, and publishing across LinkedIn and Instagram, solo. To keep that up long-term, I built a Python pipeline on FFmpeg, ElevenLabs, HeyGen, and WhisperX that automated voice, avatar rendering, captioning, and compositing, cutting manual video editing out of the process entirely.

Solution

The result was a repeatable content engine: one person producing consistent, on-brand video and image content at a cadence that would normally need a small team. Alongside it, I wrote an SEO automation script that found and resubmitted 70+ pages orphaned during a WordPress-to-Wagtail CMS migration, recovering lost search equity.

Impact

In 90 days: 35K+ LinkedIn impressions, 1,100+ unique visitors, 400+ followers, plus 14.5K+ Instagram views and 780+ interactions.

Learnings

Owning every step solo is what made the automation obvious: I only knew which parts to script because I'd felt every bottleneck by hand first. If I were scaling this again, I'd build the pipeline earlier instead of proving the manual version first.

Giving Leadership a Single View of DevOps Health

Commits Deployments Incidents Financials (+4 more) PySpark ETL Unified View

Problem

A software agency kept incident data, deployment logs, and project financials in three disconnected systems. Leadership had no unified view of DevOps health or true cost-per-project, and was effectively flying blind on which projects were quietly bleeding budget.

Process

I designed an 8-collection Lakehouse schema on MongoDB Atlas and built a PySpark ETL pipeline on Databricks to join commits, deployments, incidents, and financials. I chose 8 separate collections over one monolithic schema on purpose: it allowed clean cross-domain joins through lookup transforms without coupling the schemas together, and I pushed aggregations into PySpark SQL where document-native queries were too slow to be usable.

Solution

The pipeline produced one coherent view spanning engineering and finance, exposing failure rates, recovery times, and per-project cost that had never sat in the same place before. That view turned a vague sense of "something's off" into specific, defensible numbers.

Impact

Surfaced a 66.67% change failure rate and one project consuming 3.5x the average budget. The analysis directly supported a recommended feature freeze, which leadership accepted.

Learnings

The technical win was the schema design, but the real lesson was that the freeze got approved because the number was concrete and traceable back to source data. A defensible metric beats a sophisticated model when you need a decision made.

Skills

Python SQL PySpark MongoDB Databricks NLP / ML Prompt Engineering Google Analytics Server-Side Tracking (CAPI / RudderStack / PostHog) Agile / Scrum User Journey Mapping Stakeholder Management Market Research CRM (Apollo / Brevo)

Testimonials

"Osama consistently demonstrated a high level of dedication, support, and initiative in all tasks and projects. He has a natural talent for connecting with people, building trust, and creating positive energy around him."

Syeda Mahrukh Raza, Founder, Lean Outset (direct manager)

"Osama is a very professional yet warm-hearted colleague, always yearning for professional and personal growth while sharing that growth with the people around him. Whatever organization he joins, he outshines through his work ethic, values, and commitment."

Ahmed Abdullah, Data & BI Professional (AIESEC teammate)

Contact

I'm currently open to product and growth roles and always glad to talk shop on analytics, attribution, or AI tooling. The fastest way to reach me is email, or connect with me on LinkedIn.

Email Me LinkedIn