Data Scientist / ML Engineer

Posted 3 Days Ago
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Tel Aviv
Mid level
Artificial Intelligence • Biotech
The Role
Join a fast-growing AI startup as an ML Engineer. Collaborate with clinical data teams, analyze ML models, and implement ML-Ops pipelines.
Summary Generated by Built In

QuantHealth is an AI startup supporting drug development and clinical trials. Our ground-breaking clinical trial simulator can run thousands of trials in parallel to de-risk and optimize upcoming clinical trials. Our solution, based on a huge dataset of 350M patients and 100K drugs, drastically reduces drug development costs, shortens development timelines. We are venture backed, based in Israel, and have reputable pharma customers in both Europe and the US. This is a fantastic opportunity for a data scientist who wants to join a fast-growing startup and tackle complex challenges. 

Job Overview: We are seeking a highly skilled and creative ML Engineer to join our data science team. The ideal candidate will work closely with our clinical data science team to understand delivery requirements and deliver comprehensive evaluation and improvements to our models. This role requires strong technical skills, the ability to analyze and develop new tools for evaluating and improving machine learning models, and expertise in developing ML-Ops pipelines using MLflow. 

Key Responsibilities: 

  • Collaborate with the clinical data science team to gather requirements, understand needs and develop new ideas to integrate into model validation pipelines. 
  • Analyze and develop new tools to evaluate machine learning models. 
  • Design and implement ML-Ops pipelines using MLflow to streamline model development and deployment. 
  • Develop and integrate toolkits for model selection and evaluation. 
  • Create and maintain documentation for tools and workflows. 
  • Develop new tools to meet specific requirements and workflows. 
  • Design and implement ML systems to support clinical data science initiatives. 
  • Ensure the scalability, reliability, and performance of ML systems. 
  • Stay up-to-date with the latest advancements in machine learning and data science. 

Requirements: 

  • Bachelor's or Master's degree in Computer Science, Data Science, or a related field. 
  • At least 4 years experience as a ML Engineer / Data Scientist. 
  • Strong technical skills in Python, SQL, and Pandas. 
  • Experience with version control systems such as Git. 
  • Experience with MLflow and developing ML-Ops pipelines. 
  • Knowledge of statistical analysis and hypothesis testing. 
  • Proficiency in machine learning toolkits. 
  • Strong analytical and problem-solving skills. 
  • Experience with data visualization tools such as Matplotlib and Seaborn. 
  • Excellent communication and collaboration skills. 
  • Ability to work independently and as part of a team. 
  • Creative and innovative mindset. 

Advantage: 

  • Experience with Databricks and PySpark. 
  • Familiarity with clinical data and healthcare industry standards is a plus. 
  • Familiarity with cloud platforms such as AWS, Azure, or GCP. 
  • Willingness and ability to work closely with customers to align deliveries with their needs and expectations. 
  • Academic background in biology, bioinformatics, or a related clinical field. 

 

Top Skills

AWS
Azure
Databricks
GCP
Git
Matplotlib
Mlflow
Pandas
Pyspark
Python
Seaborn
SQL
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The Company
Tel Aviv
36 Employees
On-site Workplace
Year Founded: 2020

What We Do

QuantHealth is an AI company conducting patient-centric drug simulations to accelerate and de-risk drug development. Over 90% of drugs in clinical development stage fail to reach the market, which accumulates to a $45B/year direct lost to the pharma and biotech industry. Our platform allows our pharma and biotech partners to rapidly run thousands of variations of their clinical trials to optimize the trial design and significantly increase the probability of trial success, all while enabling discovery of new clinical opportunities and optimization strategies. QuantHealth has one the largest integrated datasets that spans the clinical, pharmacological and biological domains together with a proprietary AI platform that can predict patient-response to both approved and novel therapies.

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