Data Scientist

Job Summary:

Solves analytical problems using quantitative approaches through a combination of analytical, mathematical and technical skills. Researches, designs, implements and validates algorithms to analyze diverse sources of data to achieve project specific outcomes by leveraging statistical and predictive modeling concepts.

Key Responsibilities:

Leverages data science methodology to solve business problems. Creates individual algorithms using statistical methodologies through the use of statistical programming languages and tools. Partners with domain experts to verify model capabilities. Implements statistical techniques to clean, prepare and profile the data prior to deeper analysis. Clearly articulates results, methodologies and learnings to stakeholder and peer group. Continuous development and advancement of the team through knowledge sharing and collaboration.

Skills

• Strong analytical thinking and problem-solving skills with the ability to work on complex datasets and ambiguous business problems.

• Effective communication skills with the ability to present technical findings to diverse audiences.

• Customer-focused mindset with the ability to build strong stakeholder relationships.

• Strong decision-making capability using data-driven insights.

• Ability to manage complexity and prioritize high-value opportunities.

• Strong technical adaptability and passion for emerging technologies.

Technical Skills

• Programming: Advanced proficiency in Python (NumPy, Pandas, scikit-learn) and SQL.

• Machine Learning & AI: Strong expertise in supervised/unsupervised learning, predictive modeling, deep learning, NLP, and GenAI.

• Deep Learning Frameworks: Experience with PyTorch, TensorFlow, and/or Keras.

• LLM & GenAI Tools: Experience with Hugging Face, OpenAI APIs, LangChain, and prompt engineering.

• Data Mining & Visualization: Ability to extract patterns and insights using statistical analysis and visualization techniques.

• Statistical Modeling: Strong understanding of probability, hypothesis testing, regression, classification, forecasting, time-series analysis, and anomaly detection.

• Big Data: Experience with Spark/PySpark and large-scale data processing.

• Cloud Platforms: Experience with Azure ML, AWS SageMaker, and/or GCP Vertex AI.

• Model Deployment: Experience deploying models using MLflow, Docker, APIs (FastAPI), or similar frameworks.

• Software Development: Knowledge of version control, testing frameworks, CI/CD, and secure development practices.

Experience

• Minimum 5+ years of hands-on experience in Data Science, Machine Learning, Artificial Intelligence, or related domains.

• Proven experience building and deploying end-to-end analytical or machine learning solutions in production environments.

• Experience processing and managing large, complex datasets.

• Familiarity with business systems, industry requirements, and data governance regulations.

• Experience in Agile software development environments.

• Experience validating and testing machine learning systems for reliability and scalability.

• Familiarity with CI/CD pipelines and production ML workflows.

Nice to Have

• Experience with MLOps tools and orchestration frameworks such as Airflow or Kubeflow.

• Experience with modern data platforms such as Databricks or Snowflake.

• Knowledge of vector databases such as FAISS or Pinecone for GenAI applications.

• Exposure to RAG (Retrieval-Augmented Generation) architectures and enterprise AI deployment patterns.

• Experience working with open-source and third-party big data toolsets.

• Prior internship, co-op, student project, or relevant extracurricular experience in advanced analytics or AI is beneficial for early-career candidates.

Competencies

• Manages Complexity

• Data Mining

• Predictive Modeling

• Statistical Modeling

• Problem Solving

Competencies: 
Communicates effectively - Developing and delivering multi-mode communications that convey a clear understanding of the unique needs of different audiences.

Customer focus - Building strong customer relationships and delivering customer-centric solutions.

Decision quality - Making good and timely decisions that keep the organization moving forward.

Manages complexity - Making sense of complex, high quantity, and sometimes contradictory information to effectively solve problems.

Tech savvy - Anticipating and adopting innovations in business-building digital and technology applications.

Data Mining - Extracts insights from data by identifying relationships and patterns through use of a suite of data exploration and data visualization techniques to understand the underlying structure of the data and enable sound conclusions upon model building.

Predictive Modeling - Develops analytical or machine learning models by using appropriate variable transformations, feature selection strategies, imputation strategies, class rebalancing, resampling strategies and quality control measures to generate predictive insights used in solving business questions.

Programming - Creates, writes and tests computer code, test scripts, and build scripts using algorithmic analysis and design, industry standards and tools, version control, and build and test automation to meet business, technical, security, governance and compliance requirements.

Requirements Analysis - Evaluates relationships and interdependencies between requirements based upon their complexity and value to the business in order to determine feasibility and prioritization.

Statistical Modeling - Develops descriptive and explanatory statistical models, and simulations for regression, classification, outlier detection, anomaly detection, time series forecasting using knowledge of foundational statistics such as null hypotheses significance tests, regression models, generalized linear modeling, time series analysis, rank statistics, probability distribution fitting survival analysis, etc. to validate hypotheses for any given statistical or business question.

Problem Solving - Solves problems and may mentor others on effective problem solving by using a systematic analysis process by leveraging industry standard methodologies to create problem traceability and protect the customer; determines the assignable cause; implements robust, data-based solutions; identifies the systemic root causes and ensures actions to prevent problem reoccurrence are implemented.

Values differences - Recognizing the value that different perspectives and cultures bring to an organization. 

Education, Licenses, Certifications: 
College, university, or equivalent degree in relevant technical discipline, or relevant equivalent experience required. This position may require licensing for compliance with export controls or sanctions regulations. 

Experience: 
Relevant experience preferred such as working in a temporary student employment, intern, co-op, or other extracurricular team activities.
Knowledge of the latest technologies and trends in data science is highly preferred and includes:
- Background in processing and managing large data sets
- Knowledge of big data, open source and third party toolsets
- Experience in building analytical solutions
Experiences in the following are preferred:
- Familiarity analyzing complex business systems, industry requirements, and/or data regulations
- SQL query language
- Clustered compute cloud-based implementation experience
- Implementing Big Data platform solutions using open source and third-party tools
- Microsoft Azure and/or Amazon Web services environment
- Experience in Agile software development
- Familiarity with validation and testing of machine learning systems
- Familiarity with Continuous Integration and Continuous Delivery (CI/CD)