Data Scientist (ML, IoT)

TL;DR

Data Science Specialist (AI): Analyzing complex datasets, understanding model behavior, and driving improvements in data quality and structure for a product-focused engineering team with an accent on identifying clusters, anomalies, and dataset shifts. Focus on translating exploratory findings into clear recommendations for data filtering, relabeling, or new data collection to enhance model robustness.

Location: HITECHPARKODESA, Lviv, Odesa, Rivne, Kyiv

Company

hirify.global is a global business and technology transformation partner, helping organizations to accelerate their dual transition to a digital and sustainable world, while creating tangible impact for enterprises and society.

What you will do

• Analyze high-dimensional sensor and feature datasets using UMAP, t-SNE, PCA, and similar techniques.
• Identify clusters, anomalies, blind spots, distribution gaps, and class or environment mismatches.
• Perform data analysis aligned with classical ML models including XGBoost, SVR, k-NN, and tree-based models.
• Investigate imbalanced data, noisy sensor signals, mislabeled samples, and ambiguous cases.
• Translate exploratory findings into clear recommendations for data filtering, relabeling, or new data collection.
• Work closely with engineering teams to integrate improved data workflows into ML pipelines.

Requirements

• Master’s or PhD in Data Science, Computer Science, Applied Mathematics, Statistics, Physics, or a related field
• 2–3+ years of hands-on data science experience with real-world ML datasets including time-series, images, video, or sensor data
• Strong proficiency in Python and core data science libraries such as NumPy, pandas, matplotlib, seaborn
• Experience using dimensionality reduction and representation analysis tools such as UMAP, t-SNE, PCA
• Experience analyzing data for classical ML and deep learning pipelines
• Strong understanding of ML fundamentals, evaluation methods, and diagnostic techniques

Nice to have

• Experience with sensor or time-series data such as magnetic, radar, 3D, environmental, or IoT data
• Familiarity with scikit-learn preprocessing workflows
• Experience handling imbalanced datasets, label noise, sensor noise, and data drift
• Understanding of embedding analysis, feature importance, and model interpretability methods
• Experience working with data annotation teams or managing labeling processes

Culture & Benefits

• Health insurance from the first days, regardless of the probationary period.
• Christmas holidays from 25 December to 31 December.
• Сooperation with Superhumans center and Veteran HUB.