Quantitative Finance Analyst

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Job description

Job Description:

The Quantitative Finance Analyst will be working as a validator in Model Risk Management involving statistical and machine learning models. The role is especially designed to provide both thought leadership and hands-on expertise in methodology, techniques, and processes in statistics and machine learning including, but not limited to, Regression, Capital Model (VAR), Gradient Boosting Tress, Random Forests, Support Vector Machines, and Artificial Neural Network in anti-money laundering, fraud, operational risk, capital management and Consumer/Commercial technology and operations.

The position will be responsible for:
• Performing all model validation tasks including but not limited to independent model validation, annual model review, ongoing monitoring report review, required action item review
• Providing hands-on leadership for projects pertaining to modeling approaches for both statistical and machine learning models; and providing methodological, analytical, and technical support to effectively challenge and influence the strategic direction and tactical approaches of these projects.
• Communicating and working directly with relevant modeling teams and their corresponding Front Line Units; and if needed, communicating and interacting with the third line of defense (e.g. internal audit).

Required skills:

The successful candidate should be a modeler or validator and meet the following requirements:
• Conducted complete and rigorous independent development and/or validation of models that use machine learning methodologies.
• At least 2-years of work experience at another financial services firm in quantitative research, model development, and/or model validation.
• PhD or Masters in mathematics, statistics, computer science, and/or engineering, or possess a graduate degree in finance and/or economics with strong quantitative skills.
• Familiar with machine learning platforms/software (e.g., Python / sklearn, XGBoost, and R), algorithms, and techniques; and proficient in at least one of the following languages and statistical packages: SAS, R, and Python.
• Strong knowledge of financial, mathematical and statistical theories and practices, and a deep understanding of the modeling process, model performance measures, and model risk.
• Strong written and verbal communication skills

Job Band:

H5

Shift:

1st shift (United States of America)

Hours Per Week:

40

Weekly Schedule:

Referral Bonus Amount:

0

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Job Description:

The Quantitative Finance Analyst will be working as a validator in Model Risk Management involving statistical and machine learning models. The role is especially designed to provide both thought leadership and hands-on expertise in methodology, techniques, and processes in statistics and machine learning including, but not limited to, Regression, Capital Model (VAR), Gradient Boosting Tress, Random Forests, Support Vector Machines, and Artificial Neural Network in anti-money laundering, fraud, operational risk, capital management and Consumer/Commercial technology and operations.

The position will be responsible for:
• Performing all model validation tasks including but not limited to independent model validation, annual model review, ongoing monitoring report review, required action item review
• Providing hands-on leadership for projects pertaining to modeling approaches for both statistical and machine learning models; and providing methodological, analytical, and technical support to effectively challenge and influence the strategic direction and tactical approaches of these projects.
• Communicating and working directly with relevant modeling teams and their corresponding Front Line Units; and if needed, communicating and interacting with the third line of defense (e.g. internal audit).

Required skills:

The successful candidate should be a modeler or validator and meet the following requirements:
• Conducted complete and rigorous independent development and/or validation of models that use machine learning methodologies.
• At least 2-years of work experience at another financial services firm in quantitative research, model development, and/or model validation.
• PhD or Masters in mathematics, statistics, computer science, and/or engineering, or possess a graduate degree in finance and/or economics with strong quantitative skills.
• Familiar with machine learning platforms/software (e.g., Python / sklearn, XGBoost, and R), algorithms, and techniques; and proficient in at least one of the following languages and statistical packages: SAS, R, and Python.
• Strong knowledge of financial, mathematical and statistical theories and practices, and a deep understanding of the modeling process, model performance measures, and model risk.
• Strong written and verbal communication skills

Job Band:

H5

Shift:

1st shift (United States of America)

Hours Per Week:

40

Weekly Schedule:

Referral Bonus Amount:

0

Job Description: The Quantitative Finance Analyst will be working as a validator in Model Risk Management involving statistical and machine learning models. The role is especially designed to provide both thought leadership and hands-on expertise in methodology, techniques, and processes in statistics and machine learning including, but not limited to, Regression, Capital Model (VAR), Gradient Boosting Tress, Random Forests, Support Vector Machines, and Artificial Neural Network in anti-money laundering, fraud, operational risk, capital management and Consumer/Commercial technology and operations.

The position will be responsible for:
• Performing all model validation tasks including but not limited to independent model validation, annual model review, ongoing monitoring report review, required action item review
• Providing hands-on leadership for projects pertaining to modeling approaches for both statistical and machine learning models; and providing methodological, analytical, and technical support to effectively challenge and influence the strategic direction and tactical approaches of these projects.
• Communicating and working directly with relevant modeling teams and their corresponding Front Line Units; and if needed, communicating and interacting with the third line of defense (e.g. internal audit).

Required skills:

The successful candidate should be a modeler or validator and meet the following requirements:
• Conducted complete and rigorous independent development and/or validation of models that use machine learning methodologies.
• At least 2-years of work experience at another financial services firm in quantitative research, model development, and/or model validation.
• PhD or Masters in mathematics, statistics, computer science, and/or engineering, or possess a graduate degree in finance and/or economics with strong quantitative skills.
• Familiar with machine learning platforms/software (e.g., Python / sklearn, XGBoost, and R), algorithms, and techniques; and proficient in at least one of the following languages and statistical packages: SAS, R, and Python.
• Strong knowledge of financial, mathematical and statistical theories and practices, and a deep understanding of the modeling process, model performance measures, and model risk.
• Strong written and verbal communication skills Shift:

1st shift (United States of America)

Hours Per Week:

40