Job Description

Data Scientist

Location: Sunnyvale, CA (Remote with some Travel when needed) Duration: 12+ Months Client: Capgemini/Cepheid

We are seeking a highly skilled and motivated Data Scientist to join our team. The ideal candidate will have a strong background in data analysis, statistical modeling, and machine learning. This role involves extracting meaningful insights from complex datasets and translating them into actionable strategies that drive business growth. You will collaborate with cross-functional teams to design and implement data-driven solutions to solve real-world problems.

Qualifications:

• Education: Master's or Ph.D. degree in Data Science, Statistics, Computer Science, or a related field.
• Experience: 5+ years of experience in data science, analytics, or a related field.
• Technical Skills:
• Proficiency in programming languages such as Python or R.
• Proficiency in statistical/empirical model building
• Extensive experience with time series data, analysis, and modeling
• Strong knowledge of statistical principles and machine learning techniques (e.g., regression, classification, clustering, neural networks).
• Experience with data manipulation and analysis using libraries such as pandas, NumPy, and SciPy.
• Familiarity with data visualization tools (e.g., Matplotlib, Seaborn).
• Experience with SQL and relational databases.
• Knowledge of big data technologies (e.g., Hadoop, Spark) is a plus.
• Soft Skills: Excellent problem-solving abilities, strong communication skills, and the ability to work effectively in a team environment.
• Additional Skills (Preferred):
• Experience with cloud platforms (e.g., AWS, Google Cloud, Azure).
• Familiarity with version control systems (e.g., Git).

Job Responsibilities:

• Data Analysis: Analyze large, complex datasets to identify trends, patterns, and insights that inform business decisions.
• Model Development: Develop, implement, and validate predictive models and machine learning algorithms to solve various business problems.
• Data Cleaning: Preprocess and clean data to ensure its quality and integrity before analysis.
• Visualization: Create data visualizations and dashboards to communicate findings to stakeholders in a clear and concise manner.
• Collaboration: Work closely with cross-functional teams, including engineering, product management, and marketing, to understand their data needs and provide relevant insights.
• Reporting: Generate comprehensive reports and presentations that summarize key findings and recommendations.
• Continuous Improvement: Stay up-to-date with the latest developments in data science, machine learning, and related fields, and apply this knowledge to improve existing processes and methodologies.

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