Quantision
We are seeking an
AI Scientist
with expertise in
causal AI
to develop advanced algorithms for data-driven decision-making solutions. You will focus on building causal inference models that not only predict outcomes but also uncover the underlying causes driving those outcomes. Your contributions will enable more explainable, actionable, and robust AI systems in dynamic environments, particularly in the financial industry.
Responsibilities:
Develop and implement AI models with a focus on
causal inference
and
causal machine learning
for time series forecasting, portfolio optimization, and risk assessment.
Design and conduct
causal impact analysis
to identify relationships in financial data, ensuring that models capture true cause-effect relationships rather than simple correlations.
Collaborate with data scientists and engineers to integrate causal models into AI-driven decision-making systems tailored for financial markets.
Apply cutting-edge methods in
causal discovery ,
treatment effect estimation , and
counterfactual analysis
to evaluate investment strategies and model robustness.
Perform rigorous backtesting and validation using historical financial data to assess model performance under diverse market conditions.
Continuously fine-tune models by leveraging the latest advancements in causal AI research.
Stay updated with the latest developments in both
causal machine learning
and
financial modeling .
Requirements and Skills:
3+ years of proven experience
as an AI Scientist, Machine Learning Engineer, or a similar role, with a focus on causal AI or machine learning.
Strong expertise in
causal inference ,
causal discovery , and
counterfactual reasoning .
Hands-on experience with
financial datasets
and building AI models for the financial industry, such as asset management, hedge funds, or banking, is highly desirable.
Proficiency in programming languages such as
Python ,
R , or
Julia , and experience with libraries/frameworks like
PyTorch ,
TensorFlow , or
CausalML .
Strong mathematical foundation in
probability, statistics , and
algorithms , with a focus on causal reasoning.
Experience with
causal impact analysis
and rigorous backtesting in financial environments.
Excellent communication and collaboration skills to work across teams, sharing insights and results clearly and effectively.
A PhD or
Master’s degree in Computer Science, Statistics, Mathematics , or a related field with a focus on AI or machine learning is preferred.
#J-18808-Ljbffr
AI Scientist
with expertise in
causal AI
to develop advanced algorithms for data-driven decision-making solutions. You will focus on building causal inference models that not only predict outcomes but also uncover the underlying causes driving those outcomes. Your contributions will enable more explainable, actionable, and robust AI systems in dynamic environments, particularly in the financial industry.
Responsibilities:
Develop and implement AI models with a focus on
causal inference
and
causal machine learning
for time series forecasting, portfolio optimization, and risk assessment.
Design and conduct
causal impact analysis
to identify relationships in financial data, ensuring that models capture true cause-effect relationships rather than simple correlations.
Collaborate with data scientists and engineers to integrate causal models into AI-driven decision-making systems tailored for financial markets.
Apply cutting-edge methods in
causal discovery ,
treatment effect estimation , and
counterfactual analysis
to evaluate investment strategies and model robustness.
Perform rigorous backtesting and validation using historical financial data to assess model performance under diverse market conditions.
Continuously fine-tune models by leveraging the latest advancements in causal AI research.
Stay updated with the latest developments in both
causal machine learning
and
financial modeling .
Requirements and Skills:
3+ years of proven experience
as an AI Scientist, Machine Learning Engineer, or a similar role, with a focus on causal AI or machine learning.
Strong expertise in
causal inference ,
causal discovery , and
counterfactual reasoning .
Hands-on experience with
financial datasets
and building AI models for the financial industry, such as asset management, hedge funds, or banking, is highly desirable.
Proficiency in programming languages such as
Python ,
R , or
Julia , and experience with libraries/frameworks like
PyTorch ,
TensorFlow , or
CausalML .
Strong mathematical foundation in
probability, statistics , and
algorithms , with a focus on causal reasoning.
Experience with
causal impact analysis
and rigorous backtesting in financial environments.
Excellent communication and collaboration skills to work across teams, sharing insights and results clearly and effectively.
A PhD or
Master’s degree in Computer Science, Statistics, Mathematics , or a related field with a focus on AI or machine learning is preferred.
#J-18808-Ljbffr