Senior Manager I, Data Science

Key Skills

A/B Testing, Apache Airflow, Data Analysis, Data science techniques, Data Visualization, Docker, Effective communication skills, Google BigQuery, K-Means clustering, Kubernetes-K8s, Machine learning techniques, Microsoft-Excel, Optimization, People management, Presentation skills, Python Programming, PyTorch, R Programming, Scala Programming, Scikit-learn, Spark Core, SQL, Statistics, TensorFlow, Teradata

Job Description

What you'll do...

Position: Senior Manager I, Data Science

Job Location: 125 S Clark Street, Chicago, IL 60603

Duties:

Data Source Identification: Supports the understanding of the priority order of requirements and service level agreements. Helps identify the most suitable source for data that is fit for purpose. Performs initial data quality checks on extracted data.

Applied Business Acumen: Provides recommendations to business stakeholders to solve complex business issues. Develops business cases for projects with a projected return on investment or cost savings. Translates business requirements into projects, activities, and tasks and aligns to overall business strategy. Serves as an interpreter and conduit to connect business needs with tangible solutions and results. Recommends new processes and ways of working.

Problem Formulation: Analyzes the business problem within one's discipline and questions assumptions to help the business identify the root cause. Identifies and recommends approach to resolve the business problem. Sets data analytics, big data analytics, automation goals, and deliverables based on the established success criteria and define key metrics to measure progress and effectiveness of the solution. Quantifies business impact.

Analytical Modeling: Selects the analytical modeling technique most suitable for the structured, complex data and develops custom analytical models. Conducts exploratory data analysis activities (for example, basic statistical analysis, hypothesis testing, statistical inferences) on available data. Defines and finalizes features based on model responses and introduces new or revised features to enhance the analysis and outcomes. Identifies the dimensions of the experiment, finalizes the design, tests hypotheses, and conducts the experiment. Perform trend and cluster analysis on data to answer practical business problems and provide recommendations and key insights to the business. Mentors and guides junior associates on basic modeling and analytics techniques to solve complex problems.

Model Assessment & Validation: Identifies the model evaluation metrics. Applies best practice techniques for model testing and tuning to assess accuracy, fit, validity, and robustness for multi-stage models and model ensembles. Model Deployment & Scaling: Supports efforts to ensure that analytical models and techniques used can be deployed into production. Supports evaluation of the analytical model. Supports the scalability and sustainability of analytical models.

Data Visualization: Generates appropriate graphical representations of data and model outcomes. Understands customer requirements to design appropriate data representation for multiple data sets. Work with User Experience designers and User Interface engineers as required to build front end applications. Presents to and influences the team and business audience using the appropriate frameworks and conveys clear messages through business and stakeholder understanding. Customize communication style based on stakeholder under guidance and leverages rational arguments. Guide and mentor junior associates on story types, structures, and techniques based on context.

• Minimum education and experience required: Bachelor’s degree or the equivalent in Statistics, Analytics, Computer Science, Engineering, or related field plus 4 years of experience in analytics or related experience
• OR Master’s degree or the equivalent in Statistics, Analytics, Computer Science, Engineering, or related field plus 2 years of experience in analytics or related experience
• OR 6 years of experience in analytics or related experience.

Skills required:

• Must have experience with: Coding in an object-oriented programming language (Python)
• Performing descriptive statistical analysis in a statistical programming language (R, Python, Excel)
• Developing inferential statistics insights on business data to prove or disprove hypotheses in a statistical programming language (R, Python)
• Querying relational databases like Teradata, Bigquery using Structured Query Language (SQL) to download data for analysis in the most efficient manner
• Performing explorative data analysis (EDA) on transactional, demographic, clickstream and macro-economic datasets to develop hypotheses for statistical modeling in statistical modeling languages (R, Python, Excel)
• Performing data manipulation tasks like datatype corrections, dataset merging, dataset transposing, replacing missing values, renaming, creating new variables, etc. to prepare the dataset for statistical modeling or statistical analysis
• Developing code for data visualization tasks to convey key insights and findings from statistical analyses in a statistical programming language (R, Python)
• Developing and Communicating business analysis like sales trend analysis, year-over-year change analysis, relative comparison analysis, etc. on business data
• Developing parametric, supervised machine learning models like linear regression, logistic regression, neural networks, lasso & ridge regression, regression trees, etc. to model customer behavior used to make predictions on future customer behavior or business sales forecast
• Developing non-parametric, supervised machine learning models like classification regression trees, random forests, gradient boosting decision trees, k-nearest neighbors, etc. to model more complex, multi-variate customer behavior used to make predictions on future customer behavior or business sales forecast
• Developing unsupervised machine learning models like k-means clustering, agglomerative & hierarchical clustering, mean-shift clustering, principal components analysis, etc. to find patterns in data even without a label to indicate outcome
• Developing code to monitor, control and optimize the performance of machine learning models to avoid any under/over fitting and to ensure bias-variance is optimal, in Python
• Developing and automating pipelines to seamlessly integrate various sections of a machine learning model from data collection to producing predictions, along with quality-control modules to monitor any errors, using tools like Docker, Kubernetes and Airflow
• Developing measurement modules like A/B testing using R, Python
• Developing power point presentations for internal business partners or external clients to communicate the results and insights of a statistical or business analysis or a machine learning model and People Management. Employer will accept any amount of experience with the required skills.