Job Title: Data Analysis and Statistical Modeling Associate

Division: Advisory, Data Analytics Incubator

Summary of the Role:

The Data Analysis and Statistical Modeling (DASM) Associate is responsible for observing trends and patterns in data and in developing and implementing statistical models for data analytics projects and use cases in a professional services firm. As a key member of the Data Analytics Practice, The DASM Associate will be expected to collaborate in optimizing the use of data analytics technologies and methodologies for engagements in the following areas:

1. Internal Audit and Risk Consulting

2. Compliance and Regulatory Consulting

3. Process Quality Management

4. Transactions Advisory

5. Digital Trust Services

6. Digital Transformation

7. Audit

8. Taxation

The DASM Associate will be working alongside the other members of the Data Analytics Practice comprised of data analysts, business analysts, data visualization specialists, and data engineers in gathering, processing, and analyzing data to build statistical models that can be used to predict future outcomes, identify trends, and make business recommendations.

Primary Responsibilities:

- Develop organized methods for the experimentation and analysis of datasets to identify patterns and trends in the data, draw correlations from different patterns in the data, extrapolate likely outcomes, or estimate the effect of various possible decisions on a given metric or outcome.

- Develop and implement statistical models for data analytics projects.

- Work with business analysts and project teams to understand business problems and develop solutions involving statistical modeling and data science.

- Communicate results of analysis to project team leaders and stakeholders clearly and concisely.

- Stay up to date on the latest trends in data science and machine learning.

Qualifications:

- Bachelor's degree in Statistics or a related field.

- Strong understanding of statistical concepts and principles essential for understanding the relationships between different variables in a data set and identifying the factors that drive outcome changes. This will include fundamental concepts such as probabilities, sampling, hypothesis testing, tendency and distribution of data.

- Knowledge of statistical techniques to build models (e.g., time series analysis, linear regression, logistic regression, decision trees, random forests, etc.).

- Excellent communication and presentation skills.

- Possessing the following additional competencies is an advantage:

o Background in Risk Modeling and Validation.

o Background in data mining and machine learning techniques.

o Strong programming skills in Python, R, or other statistical programming language.

o Background in the following technology stack:

▪ Power BI ▪ Power Automate ▪ Power Query ▪ Alteryx ▪ Tableau

Please refer to job description.