Data Scientist

• Job ID:

J54655

• Job Title:

Data Scientist

• Location:

San Francisco, CA

• Duration:

12 Months + Extension

• Domain Exposure:

Banking & Finance

• Client:

To Be Discussed Later

• Employment Type:

W-2, 1099, C2C

• Work Authorization:

US Citizen, Green Card, H-1B, OPT-EAD, CPT, Other

Job Title: Data Scientist

Location: San Francisco, CA

Duration: 12 Months + Extension

Bill Rate: $90/hr C2C

Job Type: W-2, 1099, C2C

Work Authorization: US Citizen, Green Card, OPT-EAD, CPT, H-1B, H4-EAD, Other

Position Overview:

The Data Scientist will be responsible for analyzing complex datasets, developing advanced statistical models, and building machine learning solutions to support data-driven business decisions. The ideal candidate will have strong expertise in Python, statistical analysis, machine learning, predictive modeling, and data visualization.

This role involves identifying business trends, developing algorithms, performing experiments, and transforming raw data into actionable insights that improve business performance and operational efficiency.

Data Science Technologies & Technical Expertise:

• Strong programming experience with Python and data science libraries.
• Hands-on experience with Pandas, NumPy, SciPy, Scikit-learn, and Matplotlib.
• Experience with machine learning algorithms, including regression, classification, clustering, and ensemble methods.
• Strong knowledge of supervised, unsupervised, and reinforcement learning techniques.
• Experience with statistical analysis, hypothesis testing, probability, and predictive modeling.
• Knowledge of deep learning frameworks such as TensorFlow, PyTorch, and Keras.
• Experience with Natural Language Processing (NLP), text analytics, and sentiment analysis.
• Familiarity with Generative AI, Large Language Models (LLMs), prompt engineering, and RAG architectures.
• Proficiency in SQL and experience working with relational and NoSQL databases.
• Experience with data visualization tools such as Tableau, Power BI, or Plotly.
• Knowledge of big data technologies such as Apache Spark, PySpark, and Hadoop.
• Familiarity with MLOps tools such as MLflow, Kubeflow, and model deployment pipelines.
• Experience with cloud platforms such as AWS SageMaker, Azure Machine Learning, or Google Vertex AI.
• Knowledge of Docker, Kubernetes, Git, and CI/CD pipelines.

Key Data Science Responsibilities:

• Collect, process, analyze, and interpret large structured and unstructured datasets.
• Develop statistical models and machine learning algorithms to solve business problems.
• Build predictive models for forecasting, classification, recommendation, and optimization.
• Perform data cleaning, preprocessing, feature engineering, and exploratory data analysis.
• Identify patterns, trends, correlations, and anomalies within complex datasets.
• Apply statistical techniques and hypothesis testing to validate business assumptions.
• Develop NLP and deep learning solutions for text-based and unstructured data.
• Evaluate model performance using appropriate metrics and optimization techniques.
• Deploy machine learning models into production environments in collaboration with engineering teams.
• Monitor model performance and implement continuous improvements.
• Create visualizations, reports, and dashboards to communicate analytical findings.
• Collaborate with data engineers, machine learning engineers, business analysts, and stakeholders.
• Maintain technical documentation and follow responsible AI and data governance practices.

Experience & Professional Competencies:

• 6+ years of experience in Data Science, Machine Learning, Advanced Analytics, or related fields.
• 4+ years of hands-on experience using Python for data analysis and model development.
• Strong experience developing statistical models and machine learning algorithms.
• Practical knowledge of predictive analytics, feature engineering, and model evaluation.
• Experience working with large datasets and multiple data sources.
• Understanding of statistical concepts, probability, mathematics, and optimization techniques.
• Experience deploying and monitoring machine learning models is preferred.
• Exposure to cloud-based AI/ML platforms and MLOps practices is an advantage.
• Strong analytical thinking, research, and problem-solving abilities.
• Excellent communication skills with the ability to present technical findings to business stakeholders.

Academic Qualifications & Preferred Certifications:

• Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
• Master's degree or PhD in Data Science, Machine Learning, Artificial Intelligence, or Statistics is preferred.
• Relevant certifications such as AWS Certified Machine Learning Engineer, Microsoft Certified: Azure Data Scientist Associate, Google Professional Machine Learning Engineer, or similar credentials are an advantage.

Equal Opportunity

Employer

We are an equal opportunity employer. All aspects of employment including the decision to hire, promote, discipline, or discharge, will be based on merit, competence, performance, and business needs.

We do not discriminate on the basis of race, color, religion, marital status, age, national origin, ancestry, physical or mental disability, medical condition, pregnancy, genetic information, gender, sexual orientation, gender identity or expression, national origin, citizenship/ immigration status, veteran status, or any other status protected under federal, state, or local law.