• Design, develop, and optimize ML models using algorithms such as XGBoost, Random Forests, SVMs, CNNs, and Transformers • Perform hyperparameter tuning, feature selection, and algorithm evaluation • Build reproducible training pipelines with proper model, data, and experiment versioning • Extract temporal, spectral, and domain-specific features from raw sensor inputs • Apply analytical techniques such as UMAP and T‑SNE for data exploration • Model sensor-specific behaviors including noise, bias, drift, and environmental effects • Conduct ablation studies and feature importance analysis using tools such as SHAP and PDP • Develop evaluation metrics such as confusion matrices, calibration metrics, and class‑wise performance scoring

Key Responsibilities and Activities Design, develop, and execute cell-based bioassays to evaluate performance of investigative drugs (biologics) Stay updated with cutting edge discoveries and trends and contribute to the company’s R&D strategy to generate and test actionable development plans Identify emerging methods, tools, and biology approaches, and implement them when applicable Function as a key team member, and simultaneously train and supervise associates on the R&D team Collaborate closely with other scientists and research associates on the R&D team Contribute to work with external parties, including academic collaborators and CROs to create comprehensive capabilities for MoA, target validation and potency studies Write clear and concise research reports and ensure study documentation and update protocols to ELN Participate and contribute to internal discussions and meetings Present results to the project leadership team and management in an organized and timely manner Required Education, Experience, Skills and Competencies Ph.D. in Cellular Biology, Immunology, or a relevant discipline At least 2 years of industry research experience Extensive experience working with different types of in vitro systems such as immune cells, epithelial cells, fibroblasts, endothelial cells, and other cell lines Strong background with cell-based assays to interrogate immune pathway activation, immune suppression, cytotoxicity, antibody mediated cell killing Broad skillset for target validation, potency evaluation and MoA interrogations is highly desired, including the use of tissue culture approach with multiple readouts, flow cytometry, ex-vivo studies, use of gene knockdown tools, conventional immunoassays, phosphorylation analysis and conventional pulldown assays. Hands on ability to execute wet lab experiments, data processing, and ability to effectively communicate key scientific findings across the organization Ability to establish and maintain cooperative working relationships with co-workers and other stakeholders Ability to adjust to workload changes and prioritize competing demands Ability to balance the big picture and fine technical details Outstanding organizational, critical thinking, and problem-solving skills Excellent written and verbal communication skills

• Depending on Your experience level acting as Senior Data Scientist or Consultant in Delivery of AI / GenAI projects – advising on the architecture, best practices, concepts, developing solution, leading technical team, etc. Making sure customer’s goals are met with technical vitality. • Researching and preparing solutions for common DS/AI projects problems, defining the guidelines for others team. • Being up-to-date with DS and AI market trends. • Cooperate with Lingaro sales and delivery teams to identify and win new opportunities. • Offers and presentations preparation. • Developing DS&AI business through early-stage involvement into DS&AI customer journey. • DS&AI value proposition and offering creation. Promoting it within the company and outside. • Evangelization of the whole Lingaro and its customers in the field of AI/ML. • Monitoring of trends, competitors, market opportunities and preparing thought leadership content. • Helping team members and other units to achieve high customer satisfaction. • Building and maintaining relations with customers (mostly through close cooperation during project acquisition, delivery and maintenance).

• Data collection, evaluation, and annotation. • Pairwise comparisons. • Counting tasks. • Object tagging and labeling across different content types (audio, video, images, or collected data)