The Clinical Research Scientist I conducts analyses that help DeepHealth evaluate new product opportunities, understand clinical needs, and assess the performance and potential impact of AI-enabled medical imaging products.
Compensation
$110,000-$130,000 per year
Responsibilities
Collaborate with clinical, product, and technical teams to deliver analytics supporting clinical AI product validation
Query, combine, clean, and validate healthcare datasets, working with technical teams to resolve data issues and documenting definitions and limitations
Conduct exploratory and statistical analyses to characterize patient populations and clinical workflows, assess product performance, and evaluate the feasibility and potential clinical value of new product opportunities
Develop reproducible analyses and reusable tools, checking the accuracy of code and outputs and documenting assumptions, methods, and analytical decisions
Communicate findings and practical implications with non-technical stakeholders to support product decisions, clinical evidence planning, and scientific communications
Conducts analyses to support DeepHealth’s clinical AI product validation and decision-making.
Locations (2)
Somerville, Massachusetts, United States
Boston, Massachusetts, United States
Compensation
Based on experience, $110,000-$130,000
Responsibilities
Collaborate with clinical, product, and technical teams to deliver analytics supporting clinical AI product validation.
Query, combine, clean, and validate healthcare datasets, working with technical teams to resolve data issues and documenting definitions and limitations.
Conduct exploratory and statistical analyses to characterize patient populations and clinical workflows, assess product performance, and evaluate the feasibility and potential clinical value of new product opportunities.
Develop reproducible analyses and reusable tools, checking the accuracy of code and outputs and documenting assumptions, methods, and analytical decisions.
Quantitative researcher to help build out a systematic macro (futures, FX, and vol) strategies.
Compensation
The annual base salary range for this role is $150,000-$200,000 (USD) , which does not include discretionary bonus compensation or our comprehensive benefits package. Actual compensation offered to the successful candidate may vary from posted hiring range based upon geographic location, work experience, education, and/or skill level, among other things.
Responsibilities
Develop systematic trading models across FX, commodities, fixed income, and equity markets
Alpha idea generation, backtesting, and implementation
Assist in building, maintenance, and continual improvement of production and trading environments
Evaluate new datasets for alpha potential
Improve existing strategies and portfolio optimization
Requirements
Masters or PhD in mathematics, statistics, physics or other quantitative discipline
Lead the development and implementation of advanced Marketing Mix Modeling (MMM) strategies within the IT Services sector.
Compensation
Compensation: ₹15-18 LPA
Responsibilities
Design and execute complex Marketing Mix Modeling frameworks to evaluate the effectiveness of various marketing channels.
Analyze Adstock effects and calculate channel-specific contribution metrics, including Return on Marketing Investment (MROI).
Develop and run incremental budget optimization scenarios to simulate potential outcomes and guide strategic financial planning.
Create high-quality, interactive visualizations using libraries such as Matplotlib, Seaborn, and Plotly to communicate insights to stakeholders.
Build, train, and validate robust statistical models, including regression, Bayesian, and hierarchical approaches, using Python, Pandas, NumPy, and Scikit-learn.
Lead a multidisciplinary team to generate high-quality clinical intelligence and evidence across the Breast & Gynaecology portfolio using real-world evidence and AI.
Compensation
The annual base pay for this position ranges from $ 203.213,60 - 304.820,40 Annual USD. Base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. In addition, our positions offer a short-term incentive bonus opportunity; eligibility to participate in our equity-based long-term incentive program.
Responsibilities
Define and evolve the Clinical Intelligence & Evidence strategy for Breast & Gynaecology
Provide scientific leadership in epidemiology and Real-World Evidence
Work closely with drug development teams to translate clinical and portfolio questions into research objectives
Apply Real-World Evidence to support clinical development questions
Shape the appropriate use of machine learning and AI within Real-World Evidence
Requirements
Advanced degree (PhD, MPH, MD, or MSc) in Statistics, Epidemiology, Public Health, Data Science, Mathematics, or related field
Nice to have: Oncology breast and/or gynaecological cancers
The annual base pay for this position ranges from $ 203.213,60 - 304.820,40 Annual USD. Base pay offered may vary depending on multiple individualized factors, including market location, job-related knowledge, skills, and experience. In addition, our positions offer a short-term incentive bonus opportunity; eligibility to participate in our equity-based long-term incentive program.
Responsibilities
Define and evolve the Clinical Intelligence & Evidence strategy for Breast & Gynaecology
Provide scientific leadership in epidemiology and Real-World Evidence
Work closely with drug development teams to translate clinical questions into research objectives
Apply Real-World Evidence to support clinical development questions
Shape the appropriate use of machine learning and AI within Real-World Evidence
Requirements
Advanced degree (PhD, MPH, MD, or MSc) in Statistics, Epidemiology, Public Health, Data Science, Mathematics, or related field
Nice to have: Experience within Oncology, particularly Breast and/or Gynaecological cancers
Nice to have: Hands-on experience in data science, machine learning and predictive modelling
Nice to have: Familiarity with advanced approaches such as Bayesian inference, temporal modelling, multimodal modelling, foundation models, and model interpretability and evaluation
Nice to have: Experience with clinical text, electronic health records, imaging or molecular data, including genomics in clinical care or translational medicine
Doctoral Researcher in Physics-Enhanced Machine Learning for Floating Offshore Wind Digital Twins
Communicate findings and practical implications with non-technical stakeholders to support product decisions, clinical evidence planning, and scientific communications.
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