Bizoforce Hiring – Lead Data Engineer (Data Scientist) | San Jose, California, USA

Bizoforce is hiring an experienced Data Scientist to analyze complex datasets, develop predictive models, and build data-driven solutions that support business growth and operational efficiency. The ideal candidate will have strong expertise in Python, SQL, statistics, machine learning, and predictive analytics, along with experience delivering end-to-end data science solutions in enterprise environments. This role involves transforming structured and unstructured data into actionable insights, developing and deploying machine learning models, automating analytical processes, and collaborating with data engineers, product teams, and business stakeholders to solve complex business problems.

Experience (in Years)

• Required Experience: 5–15 years.
• Relevant Experience: Strong experience in data science, statistical analysis, machine learning, and predictive modeling.
• Technical Expertise: Hands-on experience with Python, SQL, TensorFlow, machine learning frameworks, and large-scale datasets.
• Model Deployment: Experience developing, deploying, monitoring, and improving machine learning models.

Required Skills

• Programming: Strong proficiency in Python and SQL.
• Machine Learning: Experience with Scikit-learn, TensorFlow, PyTorch, XGBoost, and Pandas.
• Statistical Analysis: Expertise in statistical modeling, hypothesis testing, and predictive analytics.
• Data Analysis: Experience with data wrangling, exploratory data analysis, and feature engineering.
• Machine Learning Algorithms: Knowledge of regression, classification, clustering, time-series forecasting, and natural language processing (NLP).
• Data Processing: Experience working with large datasets and cloud-based data platforms.
• Model Lifecycle Management: Understanding of data structures, algorithms, model evaluation, deployment, and monitoring.
• Data Visualization: Experience creating dashboards and visualizations using Power BI, Tableau, or Matplotlib.
• Data Quality and Governance: Ability to maintain data accuracy, quality, and compliance with organizational standards.
• Analytical Skills: Strong problem-solving, critical thinking, and data interpretation abilities.

Preferred Skills / Good-to-Have Technical Experience

• Experience with Generative AI, Large Language Models (LLMs), NLP, and Retrieval-Augmented

Generation (RAG) architectures.

• Knowledge of MLOps, model deployment, and monitoring tools.
• Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform (GCP).
• Familiarity with Apache Spark, Databricks, Snowflake, or other big data technologies.
• Experience with TensorFlow-based model development and deployment.
• Familiarity with Docker, Git, and CI/CD pipelines.
• Experience automating data science workflows and managing models in production environments.

Qualifications

• Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related field.
• Strong hands-on experience in Python, SQL, statistical analysis, and machine learning.
• Demonstrated ability to develop and deploy predictive models for business applications.
• Experience working with structured and unstructured data in enterprise environments.
• Understanding of model evaluation, performance monitoring, and continuous improvement practices.
• Strong communication skills with the ability to explain complex technical findings to non-technical stakeholders.

Key Responsibilities

Data Analysis and Business Insights

• Analyze structured and unstructured datasets to identify trends, patterns, and actionable business insights.
• Perform exploratory data analysis, data wrangling, feature engineering, and statistical analysis.
• Collaborate with business stakeholders to understand requirements and translate business problems into data-driven solutions.
• Present findings through dashboards, visualizations, reports, and presentations.

Machine Learning and Predictive Modeling

• Develop, train, validate, and deploy machine learning and predictive analytics models.
• Build forecasting, classification, clustering, recommendation, and anomaly detection models.
• Apply appropriate machine learning algorithms and statistical techniques to solve business problems.
• Design and execute experiments, A/B tests, and model evaluation frameworks.
• Evaluate model accuracy, reliability, and performance to ensure business value.

Data Pipelines and Process Automation

• Develop data pipelines and automate analytical workflows.
• Process and analyze large datasets using suitable data processing technologies.
• Collaborate with data engineering teams to improve data availability and pipeline efficiency.
• Support integration of analytical models into business applications and enterprise platforms.

Model Deployment and Performance Monitoring

• Monitor deployed models and evaluate their performance over time.
• Identify opportunities for model improvement, retraining, and optimization.
• Support model lifecycle management and production deployment processes.
• Apply MLOps practices and monitoring tools where appropriate.

Collaboration and Data Governance

• Work closely with data engineers, product teams, and business stakeholders to deliver effective analytical solutions.
• Ensure data quality, governance, and compliance with organizational standards.
• Follow best practices for code management, testing, documentation, and deployment.
• Explore emerging technologies in machine learning, Generative AI, and advanced analytics to improve business outcomes.

Assignment Details

• Job Location: San Jose, California, USA
• Work Mode: On-site
• Job Type: Full Time
• Open Positions: Multiple
• Working Hours: 9:00 AM – 5:00 PM
• Hours per Week: 40 hours
• Timesheet Cycle: Weekly
• Job Status: Active