I help people who are stuck in the wrong career move into data science without losing their minds. And I've built real production systems that saved companies millions—not as a flex, but because I know what actually works.

Six years ago, I was doing traditional engineering. Good job. Good salary. Completely unfulfilled.

Then I went all-in on data science. Taught myself. Made mistakes. Burned out. Built things nobody used. Got rejected from jobs I was overqualified for. Eventually figured out what actually matters.

Now? I'm on the other side, and I'm obsessed with helping people make that transition without going through all the chaos I did.

What makes this different: I don't sell theory. Every template, framework, and tutorial I share comes from production systems running right now. My AWS pipelines have processed billions of records. My models have prevented millions in churn. I'm not teaching from a textbook—I'm teaching from scars.

For Companies:

• Build scalable AWS data pipelines that handle millions of records without breaking
• Deploy ML models to production (not just notebooks that collect dust)
• Automate the manual processes that waste your team's time
• Reduce your cloud costs by 30-50% (yeah, that's real)
• Train teams on modern data engineering so they're not dependent on one person

For People Like You:

• Help career-changers transition into data science the right way
• Share production-tested code templates (no fluff)
• Mentor through the tough moments (imposter syndrome is real)
• Break down the "why" behind architecture decisions—not just the "what"

$2M Annual Savings | Customer Churn Prediction System A telecom company was losing 15% of customers monthly. We built an ML pipeline that identified at-risk customers before they left. 92% accuracy. 24% churn reduction. Real-time predictions in <200ms. → See the code

70% Faster Processing | Data Lake Analytics Pipeline
Large transaction datasets were taking forever to process. We automated the entire ETL pipeline with data quality checks built in. Went from days to hours. Zero maintenance after launch. → See the code

50% Faster Decisions | Real-time Sales Analytics
Sales team was working off yesterday's data. We built an automated dashboard with drill-down capabilities. Now they see performance in real-time. Decision-making got dramatically faster. → See the code

30% Cost Reduction | Demand Forecasting System Inventory levels were all over the place—too much stock, stockouts, money wasted. Built an automated forecasting pipeline that retrains itself. Now they predict demand accurately. → See the code

60% Faster Incident Resolution | Log Analytics Platform Operations team was drowning in logs. Built real-time processing with intelligent alerting. Issues get caught and fixed before they become disasters. → See the code

80% Faster Deployments | MLOps Pipeline for NLP ML models were being deployed manually—slow, error-prone, risky. Built an automated pipeline with continuous training. Models now deploy in a fraction of the time with better accuracy. → See the code

Just got back from AWS reinvent and honestly? The energy around what's possible with AI and data is wild. I'm channeling everything I learned into what I'm shipping next.

🌐 AWSWomenincloud — Co-leading a community of women from different backgrounds united by AWS learning in Buenos Aires. We're proving that tech is for everyone. If you're in Buenos Aires or want to connect with the community, let's talk.

📺 AWS Mondays — YouTube series dropping soon. Real AWS tutorials that actually solve problems. Not 45-minute videos on basic concepts. This is production-ready. Fresh ideas from reinvent already in the pipeline.

🎧 Data Conversations Podcast — Deep dives with real data professionals about what works and what doesn't. The messy reality, not the polished version.

📰 Two LinkedIn Newsletters:

• Data: a game changer — Weekly insights on data science trends and career moves
• Pulso pyme — Business analytics for SMEs (Spanish-speaking audience)

🔧 Free Resources — Career roadmap for transitioning into data science, cost optimization templates, salary negotiation scripts, production-ready code templates (actually launching these soon)

Cloud & MLOps — AWS (SageMaker, S3, Lambda, Glue, Redshift, QuickSight, Step Functions), Docker, CI/CD, Model monitoring, A/B testing

Data Science & ML — Python, SQL, PySpark, scikit-learn, TensorFlow, PyTorch, XGBoost, BERT. Specialties: time series forecasting, NLP, computer vision

Data Engineering — Apache Spark, Airflow, dbt, AWS Glue, PostgreSQL, Redshift, DynamoDB, CloudWatch, Elasticsearch

Certifications & Recognition — AWS Solutions Architect Professional, AWS ML Specialty, AWS All Builders Welcome Program recipient, just back from AWS reinvent 2025

You should talk to me if:

• You're thinking about making a career pivot into data science and want to avoid my mistakes
• Your company needs to build a scalable data strategy but doesn't know where to start
• You've got messy data and no time to figure it out
• You're building a data team and need someone who actually knows how to do this

Let's connect:

• 📧 Email: rociomnbaigorria@gmail.com
• 📅 Book a 30-min strategy call
• 💼 Follow me on LinkedIn for daily insights
• 📺 Subscribe to AWS Mondays (launching soon)
• 🎧 Subscribe to Data Conversations Podcast
• 🌐 Join AWSWomenincloud — Whether you're in Buenos Aires or anywhere else, if you're a woman interested in AWS learning, let's build this together

I'm in Argentina (GMT-3) but available for global remote work. Seriously. Let's talk.

"Making data accessible to people who actually care about using it."

I don't measure success by YouTube subscribers or Twitter followers. I measure it by the number of people who made a career move they thought was impossible, or the companies that finally have data infrastructure that doesn't make them want to cry.

That's what I'm building toward.

#DataScience #AWSCloud #MachineLearning #CareerChange #DataEngineering #CloudFirst #ProductionReady #WomenInTech