Senior Data Scientist

Company Background

Tessa Therapeutics is a clinical-stage immunotherapy company focused on the development of autologous and off-the-shelf, allogeneic therapies targeting solid tumors. Tessa’s Virus-Specific T cell (VST) platform harnesses the body’s potent anti-viral immune response and has shown compelling results in the treatment of solid tumors.

Tessa is building a portfolio of innovative, next-generation therapies by combining the qualities of VSTs with other immuno-oncology technologies. This includes a rapidly growing pipeline of clinical and pre-clinical autologous programs that target a wide range of cancers, including nasopharyngeal carcinoma, cervical cancer, oropharyngeal cancer, lung cancer, breast cancer, bladder cancer, as well as head and neck cancer. In addition, Tessa is leveraging its platform to develop an allogeneic therapy to address Epstein-Barr virus (EBV)-associated lymphomas.

Tessa has built up robust operational and supply chain capabilities to successfully deliver T cell therapy treatments to a large patient pool worldwide. Together with the Company’s academic, clinical, and commercial research partners, Tessa has created a fully-integrated approach to the treatment of cancer with immunotherapy.

Our mission is to cure cancers and save lives with our innovative and widely accessible immunotherapies. We are looking for highly motivated and enthusiastic individuals who are interested to be part of our team.

Job Position
Senior Data Scientist

Reports to
Chief Scientific Officer


  • Work with stakeholders throughout the organization to identify opportunities for leveraging company data to drive business solutions.
  • Identify major data sources at Tessa Therapeutics, and then, know, collect, cleanse and aggregate all types of data needed for analysis and mining.
  • Mine and analyze data using state-of-the-art algorithms on large datasets to extract insights and discovering patterns improving our product and clinical performance.
  • Assess the effectiveness and accuracy of data sources and data gathering techniques.
  • Design and develop predictive models using machine learning algorithms to increase and optimize clinical results and other business outcomes.
  • Develop processes and tools to monitor and analyze model performance and data accuracy.
  • Design of rigorous experiments and interpret the results to draw detailed and actionable conclusions.
  • Promote continuous improvement through data mining and analytic.
  • Present findings in a clear and concise manner to senior management to influence and strengthen business decisions.

Requirements Qualifications

  • BS or MS or PHD. in Computer Science/Engineering
  • >3 years of experience in Machine Learning Model Development or Neural Networks Model Development or 2 Years with Ph. D Background.
  • >4 years of experience on collecting, developing, analyzing, manipulating, and drawing insights from data. Also, demonstrated achievements in the fields of data science.
  • Experience with applied statistics skills, such as distributions, statistical testing, hypotheses validation, regression, etc.
  • Experience in developing models on Machine Learning and Neural Networks.
  • Experience in cloud native containers like Dockers and Kubernetes is highly desired.
  • Experience with a broad range of modeling techniques both supervised (knn, svm, trees and ensemble methods, deep learning, etc) and unsupervised methods (kmeans, hierarchical clustering, dbscan, etc)
  • Having experience in developing effective models in applications such as anomaly detection or fraud detection.
  • Experience with feature selection and reduction algorithms.
  • Practical knowledge and experience of Python/Spark Scala, specifically data science packages such as numpy, pandas, scipy, scikit-learn, NLTK, tensorflow, etc.
  • Demonstrated experience working with large, complex datasets using scalable tools related to machine learning and big data.
  • Strong ability to write complex, efficient, and eloquent SQL queries to extract data.
  • Experience with cloud computing, preferably in Google Cloud Platform.
  • Experience with Hadoop technologies.
  • Experience with RDMS, Kafka, NoSQL and Spark tools.
  • Eagerness to learn and exploration.
  • Interest to work in research environment is preferred.