Data Scientist · ML Engineer Three years building the data and recommendation layer of a live influencer marketing platform. MSc Data Science · University of Naples Federico II · Graduating October 2026
Portfolio · LinkedIn · HuggingFace · parhamkhoshsolat@gmail.com
I ship machine learning end to end. For three years that meant production work at Sakoudar, an influencer marketing platform, where I built the ingestion, the scoring and the recommendation layer for a product that is still running. Alongside it, several projects are live as interactive demos on HuggingFace Spaces. My MSc thesis is on transparency in human-robot reinforcement learning.
Data Scientist · Sakoudar · 2022 to 2025 · full time Influencer campaign management platform across LinkedIn, X and Instagram. Joined in the company's first year.
- Built the weekly ingestion across the LinkedIn, X and Instagram APIs, turning one-off snapshots into a per-influencer time series.
- Designed the scoring that made influencers comparable, weighting impressions so a daily poster did not simply outrank a weekly one, and so categories with very different audience sizes could be judged fairly.
- Built the recommendation engine: a customer brief in (budget, platforms, vertical, headcount, and the company's own mission and voice), a ranked shortlist out, matched partly on how closely a creator's language resembled the way the company talks about itself.
- Built the dashboard the founders used to check every score against what they personally knew about each influencer, and the pipeline that pushed data live only after they signed off.
| Project | Stack | What it does |
|---|---|---|
| Shielded LGV Routing | PyTorch, Bayes-DQN, CBS, PIBT, WebSocket | Six warehouse vehicles routed by nine coordination methods, classical planners against reinforcement learning, all under a PIBT collision shield that holds collisions at zero so methods compete on throughput alone. The classical planners won. Live demo |
| RL-Restore | PyTorch, DQN+LSTM, Real-ESRGAN, FastAPI, React | Reimplemented a CVPR 2018 paper: a DQN+LSTM agent repairs photos by chaining 12 specialist CNNs. Extended with baselines, the paper's unreleased joint fine-tuning, and a live web app that shows every step. Live demo |
| Florence-2 VQA | PyTorch, HuggingFace Transformers, Streamlit | Full fine-tune of Microsoft Florence-2 (230M params, every parameter trainable) for visual question answering, on 60,000 image-question pairs from VQA v2.0. Live demo |
| Retail Geospatial Analytics | GeoPandas, Folium, Python, SQL | Industry analytics challenge with Fater S.p.A. (Procter & Gamble × Angelini Industries). Joined proprietary sales data with public census data at district level, and designed a per-capita metric so districts of different sizes compared fairly. Presented the findings to company leadership; the four-person team was recognised for the project. |
| Pest Population Forecasting | Scikit-learn, XGBoost, LightGBM, Streamlit | Benchmarked six regression and five classification models on noisy multi-source sensor data. Random Forest topped both, catching every outbreak day in the held-out set at 0.92 AUC and roughly 50 percent precision, with the threshold tuned to favour recall. Live demo |
| Stock Clustering Pipeline | Apache Kafka, PySpark, scikit-learn | Kafka and Zookeeper cluster ingesting six months of daily prices across 55 per-ticker topics, plus a separate PySpark MLlib K-means and PCA stage on a ticker's price series. |
| OULAD Time-Series Forecasting | TensorFlow / Keras, Statsmodels, Prophet | Benchmarked SARIMA, ARIMAX, Prophet and a custom 1D CNN on student interaction data. The CNN and Prophet came out closest; only the CNN was scored on a held-out split, so it is not a like-for-like comparison. |
Languages: Python, SQL, JavaScript, Bash ML / Deep Learning: Recommender systems, text similarity, sentiment analysis, PyTorch, HuggingFace Transformers, Scikit-learn, XGBoost, LightGBM, TensorFlow / Keras, Random Forest, LSTM, GRU, CNN, Reinforcement Learning, Fine-tuning, Transfer Learning Data Engineering: Apache Kafka, PySpark, ETL, REST APIs, GraphQL, MySQL Visualisation & BI: Power BI, Plotly, Seaborn, Matplotlib, GeoPandas, Folium, Streamlit MLOps: Docker, FastAPI, HuggingFace Spaces, Git / GitHub workflows, Colab GPU training
- MSc Data Science at Federico II (in progress, expected Oct 2026) · weighted average 28.67/30 · four exams at 30 e lode
- 5G Academy · Federico II with Nokia, TIM, and PagoPA (currently attending)
- Apple Foundation Program · Federico II × Apple Developer Academy (Jan 2025)
- BSc Information Technology Engineering · Amol University (2017)
Working on my MSc thesis: research in human-robot interaction and reinforcement learning, extending published work from Federico II.
Open to Data Analyst, Data Scientist, ML Engineer, or AI Engineer roles starting now. Onsite Naples or remote across the EU.