Data Scientist in IOT

**Hard Skills:**

- IoT Data Analytics & Visualization

- Machine Learning & Deep Learning (TensorFlow, PyTorch)

- Time Series & Signal Processing

- Edge Computing & Microcontroller Programming (Arduino, Raspberry Pi)

- Cloud Platforms (AWS IoT, Azure IoT)

- Data Pipeline Development (Kafka, Apache NiFi)

- SQL & NoSQL Databases (TimescaleDB, MongoDB)

- Python (pandas, scikit-learn, statsmodels) & R

- Rest API & MQTT Protocols

**Soft Skills:**

- Problem-Solving & Critical Thinking

- Cross-Functional Collaboration

- Technical Documentation & Data Storytelling

- Agile Methodologies

- Adaptability in Fast-Paced Environments

*Senior IoT Data Scientist*

*InnovateSense Technologies, Seattle, WA*

June 2022 – Present

- Architected end-to-end data pipelines transforming heterogeneous sensor data into centralized analytics dashboards, reducing latency by 35%.

- Led the development of predictive maintenance models for manufacturing sensors, decreasing unplanned downtime by 22%.

- Implemented real-time anomaly detection using LSTM neural networks, improving device failure prediction accuracy.

- Collaborated with hardware teams to optimize sensor configurations, leading to a 15% increase in data fidelity and network efficiency.

IoT Data Scientist

*NextGen Analytics, San Francisco, CA*

August 2018 – May 2022

- Designed machine learning models to analyze IoT sensor data for smart building management, reducing energy usage by 18%.

- Developed a scalable MQTT-based data ingestion system applying Kafka for high-throughput processing.

- Created interactive dashboards for live IoT device metrics, facilitating quick troubleshooting and informed decision-making for clients.

- Conducted A/B testing on edge devices to identify optimal data sampling strategies, resulting in a 25% decrease in bandwidth consumption.

*Data Analyst (Intern)*

*SmartGrid Solutions, Boston, MA*

Summer 2017

- Assist in analyzing grid sensor data to forecast power loads, contributing to grid stability predictions.

- Automated data cleaning scripts, improving data reliability for downstream analysis.

**Master of Science in Data Science**

University of California, Berkeley, CA

*2016 – 2018*

**Bachelor of Science in Electrical Engineering**

Massachusetts Institute of Technology, Cambridge, MA

*2012 – 2016*

- AWS Certified Machine Learning – Specialty (2023)

- Certified IoT Professional (CIoTP) – IoT Institute (2022)

- Deep Learning Specialization – Coursera (2021)

**Smart Fleet Monitoring System**

Devised a fleet IoT monitoring platform utilizing edge devices for real-time telemetry, predictive analytics for vehicle maintenance, and cloud-based dashboards, resulting in a 30% reduction in operational costs.

**Industrial IoT Sensor Optimization**

Led a project optimizing sensor placement and data sampling rates across manufacturing lines, improving data accuracy and reducing network load by 20%, enhancing predictive model performance.

**Edge-Based Anomaly Detection**

Developed a lightweight anomaly detection model deployable on microcontrollers, enabling real-time fault detection directly at sensor nodes, minimizing data transmission costs.

- English (Native)

- Spanish (Professional Working Proficiency)

*References available upon request.*