Hello! I'm Sitraka FORLER, a French Data & IT professional passionate about
building intelligent systems that create real-world impact. With expertise spanning Data Science,
Machine Learning, Cloud, and Cybersecurity, I help organizations unlock the value hidden in their
data.
Alongside my consulting career, I am the Co-Founder of LuxAiOps, a startup dedicated
to bringing intelligent IT Operations and automation to Luxembourg and Europe. I also take on
freelance projects — delivering tailored, production-ready solutions to clients.
Applied NLP (Hugging Face, Keras) to product descriptions.
Built predictive models and web traffic KPI dashboards.
Dec 2020 — Dec 2020 | France
👨💻 Ethical Hacker
Hack the Crisis France
Hackathon! 1 full week, non stop, during the Covid Crisis. Ideation,
Business Plan, Prototypying, Pitching and team working. 5th on 34 participating groups.
Engineered KPI Dashboards: Designed and deployed two interactive
TIBCO-Spotfire dashboards to monitor real-time production line performance indicators.
Scripted complex data processing logic using R, customized backend functionalities with
.NET, and structured the frontend user interface utilizing HTML.
Automated Reporting Pipelines: Developed an automated distribution
system to route critical service documents and KPIs directly to Industrial Engineers and
Plant Managers, streamlining communication and eliminating manual reporting overhead.
Predictive Maintenance Modeling: Spearheaded a data-driven project to
forecast production tool breakdowns. Leveraged Python and SQL to extract and analyze
historical machine data, identifying early indicators of equipment failure to optimize
production uptime.
Skills: Indicateurs clés de performance · Cybersécurité · R · Tableaux de
bord d’indicateurs clés de performance · Python · SQL · KPI
Oct 2018 — Jun 2020 | Greater Marseille Metropolitan Area
💼 Consultant / Junior Data Analyst
Aix Marseille Strategy Consulting Group (AMS CG)
Implementation of data projects for clients: Data cleansing (Corsica Ferries, Nexity)
and Customer feedback analysis (PSA Group).
Implementation of a Gantt chart for a team of 20 stakeholders.
Performing analysis to assess quality and meaning of data.
Filter Data by reviewing reports and key performance indicators (KPI) to identify and
correct trends and threats.
Using statistical tools to identify, analyze, and interpret patterns and trends in
complex data sets that could be helpful for the diagnosis and prediction.
Preparing annual, quarterly and monthly financial projections for designated client
groups, explaining variances with the budget and financial forecasts.
Conduct business meetings with customers to determine requirements and milestones.
Ensure the quality of the delivery by conducting tests (including performance test),
troubleshooting and integrating new elements.
Skills: Microsoft Excel · Cybersécurité · Python · SQL
May 2019 — Jul 2019 | Région de Marseille, France
🔬 Research Assistant
Aix-Marseille School of Economics · Internship
The para-demographic productivity gap in the developing world: an international
comparison
During that internship I was under the supervision of Pr. Gilles Dufrenot at the
Aix-Marseille School of economics. We had to analyse the previous and actual point of
view of existing scientific papers, then we worked on the econometrics regressions and
interpretations.
Our paper investigates the heterogeneous productivity gaps by panel stochastic frontier
models to confirm a para demographic “burden” through inefficiencies in the labor
productivity in the developing countries. As a result, in terms of productivity,
Sub-Saharan Africa does not benefit from a demographic dividend.
Order of Authors: Kimiko Sugimoto; Gilles Dufrénot; Sitraka Forler
Python is the cornerstone of my data science and machine learning workflows. I specialize in
building end-to-end ML pipelines, from data wrangling with pandas and
NumPy to training predictive models. Using tools like
Scikit-Learn and XGBoost, I focus on delivering models that
bring actionable business value and optimize operations.
train_model.py
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
import pandas as pd
# Load data and train-test split
df = pd.read_csv('operational_data.csv')
X_train, X_test, y_train, y_test = train_test_split(df.drop('target', axis=1), df['target'], test_size=0.2)
# Initialize and train model
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
# Predict and evaluate
predictions = model.predict(X_test)
print(f"Model Accuracy on Test Set: {accuracy_score(y_test, predictions):.2%}")
🧠 Deep Learning
Deep Learning allows me to solve complex perceptual problems such as NLP and Computer Vision. I
leverage PyTorch, TensorFlow, and Hugging
Face to develop, fine-tune, and deploy state-of-the-art neural networks. My recent
experience includes deploying large language models (LLMs) into production environments and
building retrieval-augmented generation (RAG) applications.
resnet_block.py
import torch
import torch.nn as nn
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels):
super(ResidualBlock, self).__init__()
self.fc1 = nn.Linear(in_channels, out_channels)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(out_channels, out_channels)
def forward(self, x):
identity = x
out = self.fc2(self.relu(self.fc1(x)))
return out + identity # Skip connection
model = ResidualBlock(128, 128)
print(model)
☁️ Cloud Architecture
Building scalable and robust backend systems is critical for modern data solutions. I design and
implement cloud-native architectures on AWS and Azure,
employing Infrastructure as Code (IaC) to ensure reproducible and secure deployments. This
includes setting up CI/CD pipelines, container orchestration with Kubernetes,
and adopting serverless architectures.
A solid data layer is the foundation of any application. I have extensive experience modeling
business requirements into both relational databases (like PostgreSQL and
MySQL) and NoSQL datastores (like MongoDB and
Cassandra). My expertise covers advanced querying, query optimization, dynamic
indexing, and designing scalable data warehouses.
revenue_analysis.sql
WITH MonthlyRevenue AS (
SELECT
DATE_TRUNC('month', transaction_date) AS month,
SUM(amount) AS total_revenue
FROM financial_transactions
WHERE status = 'COMPLETED'
GROUP BY 1
)
SELECT month, total_revenue,
LAG(total_revenue) OVER (ORDER BY month) AS prev_month_revenue,
(total_revenue - LAG(total_revenue) OVER (ORDER BY month)) / LAG(total_revenue) OVER (ORDER BY month) * 100 AS mom_growth_pct
FROM MonthlyRevenue
ORDER BY month DESC;
🔐 Cybersecurity
Security must be integrated from the ground up. I have hands-on experience using tools like
Splunk and Venafi to implement proactive threat detection,
manage certificates, and secure data in transit. My focus is on embedding security within the
DevOps lifecycle (DecSecOps) to preempt vulnerabilities.
auth_security.py
import hashlib
import os
def secure_hash_password(password: str) -> str:
# Generate a cryptographically strong random salt
salt = os.urandom(16)
# Create PBKDF2 hash using SHA-256
key = hashlib.pbkdf2_hmac(
'sha256',
password.encode('utf-8'),
salt,
100000 # 100,000 iterations
)
return salt.hex() + ':' + key.hex()
📈 FinTech & Quant
Applying mathematical and statistical methods to financial markets is a specialization of mine. I
build models for risk assessment, algorithmic trading, and portfolio optimization. Leveraging
real-time data ingestion and time-series analysis, I deliver insights that directly impact
financial decision-making.
LuxAiOps
Pioneering AIOps
(Artificial Intelligence for IT Operations) in Luxembourg and across Europe.
🚀 Our Vision
Modern IT infrastructure is becoming increasingly complex. At LuxAiOps, we believe
the solution is leveraging AI/ML to automate monitoring, detect anomalies before they cause
downtime, and streamline IT operations. We are building the next generation of intelligent tools for
enterprise resilience.
🛠️ Core Offerings
Predictive Analytics: Forecasting system failures and resource bottlenecks
before they impact the business.
Intelligent Automation: Automated root-cause analysis and self-healing systems.
Secure & Compliant AI: Solutions tailored to the strict regulatory
requirements of the European market (GDPR, DORA).
MLOps Consulting: Helping companies deploy and scale their own machine learning
models reliably.
