Machine Learning Engineer

Associate - Data Scientist - IN
The Data Scientist participates in the effort to obtain business insights from large data sets through complex data research and analysis. Under general supervision, this role contributes to development of data mining and data analysis methodologies to create algorithms and predictive models. This job is responsible for analyzing the current data mining and data analysis methodologies to implement new processes as needed.

Key Responsibilities and Duties

• Develops and implements data mining protocols, architectures, and models as well as data analysis methodologies, used to identify trends in large data sets.
• Cooperates in developing new data mining and data analysis processes as needed to improve effectiveness and accuracy of data analyses, based on research of existing and emerging data science principles, theories and techniques.
• Prepares statistical reporting, data analyses insights, and data visualizations, used to better understand client relationships and improve client services and product offerings.
• Consulting on using business intelligence data for predictive analytics and facilitating implementation of new tools.
• Inspects the acquisition of data from structured and unstructured sources, while ensuring quality and comprehensiveness of the data.
• Coordinates research and analytic activities utilizing various data points and employ programming to clean and organize data.

Educational Requirements

• University (Degree) Preferred

Work Experience

• 2+ Years Required; 3+ Years Preferred

Physical Requirements

• Physical Requirements: Sedentary Work

Career Level
6IC

Position Summary:  Describe below the primary purpose and function of this job

We are looking for a Machine Learning Engineer with expertise in traditional Artificial Intelligence (AI) techniques, such as statistical modeling, classical machine learning algorithms, and optimization methods. The ideal candidate will have a strong foundation in implementing, deploying, and fine-tuning traditional AI solutions to solve real-world problems, while also being familiar with modern tools and techniques for AI/ML workflows.  

This role focuses on leveraging tried-and-true AI methods to build efficient and interpretable models, process data, and improve decision-making systems across various business domains.  

Key Duties & Responsibilities:  List up to 5 key duties and responsibilities, management responsibilities and time spent (if applicable)

1. AI/ML Model Development:  
  - Design, implement, and optimize traditional AI algorithms such as regression models, decision trees, random forests, support vector machines (SVM), clustering (e.g., K-means), and ensemble methods.  
  - Leverage techniques like dimensionality reduction (e.g., PCA, LDA), and optimization algorithms to improve model performance.  

2. Data Processing and Feature Engineering:
  - Work on pre-processing structured and unstructured datasets using techniques like data cleaning, normalization, and feature extraction.  
  - Identify and engineer relevant features for classical machine learning models.  
3. Model Deployment & Monitoring:  
  - Deploy models into production systems and ensure their scalability and reliability.  
  - Monitor and maintain deployed models to ensure long-term performance.  

4. Evaluation and Validation:  
  - Use statistical and validation methods (e.g., cross-validation, A/B testing) to evaluate model accuracy and generalization.  
  - Interpret results and provide actionable insights.  

5. Collaboration and Documentation:  
  - Collaborate with data scientists, software engineers, and product teams to integrate AI models into applications.  
  - Document model design, implementation details, and performance results comprehensively.  

6. Optimization & Efficiency:  
  - Focus on designing lightweight, explainable models that prioritize computational efficiency.  

Management/Leadership Responsibility:  Is management of people a primary focus of the role?  If so, how many direct and indirect employees are managed?  Do any of them manage a function or process?

NA

Budget Responsibility: Does the position have responsibility for Revenue, Operating (expense) Budget, etc.?  If so, what is the scope?

N/A

Impact:

NA

NA

Business or Industry Expertise:  Describe the degree of knowledge and understanding required of TIAA’s business and industry, commercial environment and of competitors products and services.

Interactions / Interpersonal Skills:  Describe the nature and level of interactions this job has with others, both internally and externally.  Explain any specific interpersonal skills necessary to successfully perform this role (i.e., negotiation skills, represents business at external events or to governmental bodies, etc. ).

Job Requirements And Qualifications:  Indicate the minimum and preferred education and experience for the job and any licenses and certifications required

Required Education:

Choose an item.

(add “other” details here)

Preferred Education:

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Skills and Abilities:

• 5-7+ Years of relent experience in the field.

AI/ML Expertise:
- Strong understanding of classical machine learning techniques such as:  
 Linear Regression, Logistic Regression, Decision Trees, Random Forests, Gradient Boosting (e.g., XGBoost, LightGBM), K-Means, and SVM.  
- Experience in statistical modeling, including methods like Bayesian inference, hypothesis testing, and time series forecasting.  
- Familiarity with optimization algorithms (e.g., Gradient Descent, Genetic Algorithms, Simulated Annealing).  
- Knowledge of evaluation metrics for classification, regression, and clustering tasks (e.g., ROC-AUC, MSE, silhouette score).  

Programming and Tools:
- Proficiency in programming languages such as Python (NumPy, Pandas, scikit-learn, statsmodels) or R.  
- Experience with machine learning libraries and frameworks (e.g., scikit-learn, XGBoost, LightGBM).  
- Familiarity with data visualization tools (e.g., Matplotlib, Seaborn, Plotly).  


Mathematical and Statistical Knowledge:
- Strong foundation in linear algebra, calculus, probability, and statistics.  
- Familiarity with statistical tools for hypothesis testing and model validation.  

Other Skills:
- Excellent problem-solving and analytical skills.  
- Strong written and verbal communication abilities to present technical results to non-technical stakeholders.  
- Experience in version control systems like Git.  

Preferred Qualifications:
- Familiarity with deep learning frameworks such as TensorFlow or PyTorch (even if secondary).  
- Understanding of natural language processing techniques using traditional AI (e.g., TF-IDF, Latent Dirichlet Allocation (LDA)).  
- Knowledge of explainability tools like SHAP or LIME.  
- Exposure to cloud platforms (e.g., AWS, Azure, or Google Cloud) for deploying ML pipelines.  
- Experience with time series forecasting tools (e.g., ARIMA, Prophet).  

• Data Engineering Skills:
  - Proficiency in SQL for data querying and manipulation.  
  - Familiarity with big data tools like Spark or Hadoop is a plus.  
  
  Familiarity with one or more data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo, etc.)
• Ability to translate business needs to technical requirements
• Strong understanding of software testing, benchmarking, and continuous integration
• Exposure to machine learning methodology and best practices

A bachelor's or master's degree in computer science, data science, information science or related field, or equivalent work experience.

Related Skills

Application Programming Interface (API) Development/Integration, Automation, Communication, Consultative Communication, Containerization, DevOps, Enterprise Application Integration, Influence, Organizational Savviness, Problem Solving, Prototyping, Relationship Management, Scalability/Reliability, Software Development Life Cycle, Systems Design/Analysis

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Company Overview

TIAA Global Capabilities was established in 2016 with a mission to tap into a vast pool of talent, reduce risk by insourcing key platforms and processes, as well as contribute to innovation with a focus on enhancing our technology stack. TIAA Global Capabilities is focused on building a scalable and sustainable organization , with a focus on technology , operations and expanding into the shared services business space.

 
Working closely with our U.S. colleagues and other partners, our goal is to reduce risk, improve the efficiency of our technology and processes and develop innovative ideas to increase throughput and productivity.

We are an Equal Opportunity/Affirmative Action Employer. We consider all qualified applicants for employment regardless of age, race, color, national origin, sex, religion, veteran status, disability, sexual orientation, gender identity, or any other protected status.

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If you are a U.S. applicant and desire a reasonable accommodation to complete a job application please use one of the below options to contact our accessibility support team: 

Phone: (800) 842-2755

Email: accessibility.support@tiaa.org

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