Quantitative Analytics Senior Cover Letter

In response to your job posting for quantitative analytics senior, I am including this letter and my resume for your review.

In the previous role, I was responsible for analytical and strategic commercial modeling of market performance for IBIGSA, affiliate market environments and business models to assess current performance and continuously develop sound commercial forecasts.

Please consider my experience and qualifications for this position:

• Knowledge of Statistical programming software packages such as SPSS, SAS, STATA, or R
• Knowledge of data programming languages such as Python, R, SQL, or MATLAB would be an added advantage
• Experience using SAS and other analytical tools along with Microsoft Office applications
• Project Management / Interpersonal skills
• Excellent understanding of machine learning and data mining such as decision trees, ensemble learning algorithms (random forest and boosting methods), neural networks and feature selection/engineering
• Hands-on experience with SAS/Python/R/SQL
• Can work under pressure and manage project effectively and efficiently
• Solid background in statistics including regression and linear models, statistical tests, and performance metrics

I submit this application to express my sincere interest in the quantitative analytics senior position.

Previously, I was responsible for hands on modeling and support for analytics initiatives to accelerate performance by building predictive models that quantify the impact of sales actions and identify commercial opportunities.

My experience is an excellent fit for the list of requirements in this job:

• Proficient user of SAS, SQL, Hadoop, Scala, Spark, R, Python and JAVA
• Excellent quantitative skills, solid knowledge of financial derivatives, pricing theory, market and credit risk management methodologies depending on specific business area
• Able to work well in a team environment or as an individual contributor
• Track record of impacting change through leadership and organizational skill
• Experience using Python to consume APIs
• DevOps experience and working knowledge of version control tools (Git/Bitbucket)
• Prior Capital Markets and Market Risk experience is beneficial for success in this role
• A curious self-starter with strong attention to detail, an owner's mindset who can produce top quality work with limited direction but also work very well as part of a team

Please consider me for the quantitative analytics senior opportunity. I am including my resume that lists my qualifications and experience.

In my previous role, I was responsible for senior level support in the identification and assessment of risks and recommends policies for modelling these risks.

I reviewed the requirements of the job opening and I believe my candidacy is an excellent fit for this position. Some of the key requirements that I have extensive experience with include:

• Proficient programming such as SAS/Python/R/SQL Microsoft Office applications
• Strong working knowledge and hands-on experience using SAS or SQL in the context of data manipulation, data mining, statistical analysis, and predictive modelling
• Strong knowledge of modern statistical model development and validation concepts and techniques (particularly linear and logistic regression)
• A working knowledge of concepts and methodologies (such as retail credit risk scoring techniques) used in the assessment of credit risk for retail credit exposures
• Strong understanding of advanced analytics software such as R or SAS
• Significant experience in understanding and analyzing data
• Significant experience building mathematical models
• Data management and manipulation skills

I submit this application to express my sincere interest in the quantitative analytics senior position.

In my previous role, I was responsible for analytical and strategic commercial modeling of market performance for EMEAP, affiliate market environments and business models to assess current performance and continuously develop sound commercial forecasts.

My experience is an excellent fit for the list of requirements in this job:

• Locate, extract, and transform data for ad-hoc analyses in support of Risk and Small Business initiatives
• Use analytical and statistical methods to identify emerging risks, optimize existing models or strategies, and develop new models or strategies (including originations models, auto-adjudication strategies, portfolio management)
• Document model/strategy development, measure business impacts, make recommendations for implementation, present/communicate to key stakeholders, advocate for adoption or implementation, test the implementation, defend model/strategy during review by Model Validation, Credit Review, and Audit (internal and external) where applicable
• Develop interactive dashboards and reporting to monitor strategies and models used in originations and portfolio management
• Support model/strategy users by communicating limitations and weaknesses and providing insights into why specific decisions/actions were taken
• Previous experience in credit risk analytics a definite asset
• Proven experience as a Machine Learning Engineer or similar role
• Deep understanding of data structures, data modeling and software architecture

In response to your job posting for quantitative analytics senior, I am including this letter and my resume for your review.

In my previous role, I was responsible for technical and data analytics leadership in the interpretations of trends observed in commercial process monitoring (e.g., Big data analysis, visualization, advanced mathematical models, statistical process control (SPC) and/or multivariate analyses (MVA)), and technical investigations.

Please consider my qualifications and experience:

• Some cloud (AWS, Azure, ) experience
• Some experience in quantitative modeling and development (especially pricing and market risk), quantitative finance, banking, – strongly preferred
• Proficiency and extensive experience in creating and manipulating large data sets for data mining and statistical modelling from various internal and external source systems
• Working knowledge of concepts and methodologies, such as non-retail credit risk scoring techniques, used in the assessment of credit risk
• Working knowledge of Basel parameters (PD, LGD, EAD)
• Strong background in data-driven statistical modeling, supervised learning, unsupervised learning and discriminative models
• Programming experience is mandatory
• Knowledge of coding standards and object-oriented programming