In [1]:cells/00_intro.py

python 3.12 · idle

portrait.png

online 1:1

# ~/jnacer

Jorge Nacer. Data Scientist & ML Engineer shipping production ML systems.

Hamburg-based. I build predictive systems end-to-end, from framing the business problem to the model in production and the alert that pages when it drifts. Happiest in the seam between research code and reliable infrastructure.

open to opportunities py · pytorch · k8s · aws/gcp ES (Native) · EN (C1) · DE (A2)

Get in touch Browse projects ↓ cv.pdf

fig.01 loss surface · SGD trajectory live

step 0

loss -

lr 3e-3

opt sgd+mom

In [2]:cells/01_projects.py

featured

## projects

Selected work

A mix of shipped systems and open-source tools. Click any card to expand.

PRJ · 01 · 2025→

Synthesis Overthrust

● open-source · AGPLv3

Gamified skill-development platform with structured skill-tree covering MLE, DS, DE, AI Engineer, and custom tracks. Six seniority tiers and spaced-repetition scheduling inspired by RPG progression.

Tauri 2 / RustReact + TSFastAPISQLite

roles 4tiers T1→T6nodes ~380

Problem. Career development for data/ML roles is fragmented — there's no single map from fundamentals to specialization or expertise.

Approach. Model each role as a dependency graph of skills with prerequisites; schedule reviews via SM-2 spaced repetition; render the whole thing as a zoomable skill-tree.

What I built. Desktop shell in Tauri, Python service for progression tracking, graph-diff engine for detecting prerequisite conflicts, and a ritualized "level-up" flow for milestone celebrations.

In [3]:cells/02_stack.py

stack.toml

## stack

Tools I reach for

Opinions earned the hard way. I care more about boring-correctness than novelty - but I'll swap any of these for a better fit.

languages

Python (primary, ML/backend, NumPy/SciPy, Pydantic, AsyncIO) · SQL (window funcs, CTEs, optimization) · Bash (automation, CI/CD) · C++

ml / dl

PyTorch (training, fine-tuning) · MLflow (tracking, registry) · TensorFlow · scikit-learn, XGBoost, LightGBM · RNN / LSTM · Optuna · Causal Inference · HF Transformers (NLP, RAG, LLM fine-tuning) · Model Optimization (ONNX, TensorRT, quantization)

data

Apache Spark (PySpark) · Apache Kafka (streaming) · Polars, pandas, NumPy, SciPy · Airflow orchestration · dbt · ETL/ELT

platforms

Kubernetes (+ Kubeflow) · Docker · Terraform · AWS (SageMaker, S3, EC2, Lambda, Athena) · GCP (Vertex AI, BigQuery, Cloud Run, GCS)

ml ops

MLflow tracking + registry · Airflow · CI/CD (GitHub Actions, GitLab CI) · Pydantic contracts

databases

Snowflake / BigQuery (ELT, analytics, feature stores) · Redis (real-time features) · DynamoDB (metadata)

vector / llm

ChromaDB, Pinecone · RAG and agentic workflows · fine-tuning LLMs

observability

Prometheus + Grafana · Cloud Monitoring · structured logging · model + data drift alerts

development ecosystem

Ruff, Black, Mypy, Pytest · Git (branching, collaborative workflows) · type-first, test-first, review-first · design patterns

# how I actually pick tools
def choose(problem):
    if problem.is_novel:
        return "proven tool, novel composition"
    if problem.is_standard:
        return "the team's most-loved standard tool"
    return "Ops transparent, optimized for fast debugging at 3am"

In [4]:cells/03_timeline.py

experience.log

## timeline

Where I've been

11.2024 - 04.2025 · Hamburg (Remote)

Senior Data Scientist - Leprcon

Architected data-integrity and analysis pipelines for energy analytics. Anomaly detection via statistical validation (Pandas, Statsmodels); hypothesis testing with scikit-learn to mitigate technical and business risks; translated statistical findings into actionable recommendations.

PandasStatsmodelsscikit-learnEnergy analytics

01.2022 - 02.2024 · Santiago (Remote)

Senior Data Scientist - Walmart Chile

MLOps for customer analytics: multi-container Docker/Kubernetes pipelines on Vertex AI (GCP), monthly CLV batch predictions with LightGBM, loyalty budget optimization. Maintained 99.9% data-quality SLOs via automated BigQuery SQL and Cloud Monitoring.

GCP / Vertex AIKubernetesLightGBMBigQueryMLOps

07.2020 - 12.2021 · Santiago

Data Scientist - Walmart Chile

Built LightGBM churn models; engineered K-means and DBSCAN customer segmentations; ran A/B testing with Statsmodels/SciPy and diff-in-diff to drive marketing ROI.

LightGBMK-means / DBSCANA/B testingCausal inference

2018 - 2019 · Valparaíso

M.Sc.-level coursework, Informatics Engineering - UTFSM

Post-graduate coursework in Machine Learning and Deep Learning; completed all advanced coursework and examinations with a focus on scientific research and engineering theory.

MLDeep LearningResearch

09.2016 - 02.2018 · Santiago

Software Engineer - Qservus

Production-ready REST APIs with Django, SQL, and Elasticsearch. Webpay integrations via Celery + Redis for async processing. Django web-app maintenance and Git-driven team workflows.

DjangoElasticsearchCelery / Redis

03.2015 - 09.2016 · Santiago

Backend & Frontend Developer - Jumpitt Labs

Backend for fitness and retail platforms in Python (Django) and PHP (Laravel); RESTful APIs integrating SQL DBs; end-to-end AWS deployments on EC2/S3 with Docker and Git versioning.

Django / LaravelAWS EC2 / S3DockerVue.js

- 2014 · Valparaíso

Informatics Engineering- UTFSM

6-year Engineering degree; focused on AI and quantitative methods; includes B.Sc.-equivalent in Computer Science.

CSAIQuantitative methods

In [5]:cells/04_contact.py

contact.yml

## contact

From prototype to resilient infrastructure

Best for production ML systems, forecasting, RAG and retrieval, MLOps works. Whether you need end-to-end development or a "second opinion", I offer a pragmatic, engineering-first perspective. I reply inside 48 hours, and I'll tell you honestly if I'm not the right fit.

calendly

book a 30-min intro call

schedule

email

jorge.nacerc@gmail.com

github

@on1link

open

/in/jorge-nacer

open

gitlab

@jorge-nacerc

open

cv

Nacer-Jorge-CV.pdf

download