ZipRecruiter
Data Scientist (The Data Whisperer)
ZipRecruiter, San Francisco, California, United States, 94199
Job Description
Are you a data enthusiast who loves uncovering hidden insights from vast datasets? Do you thrive on turning data into actionable insights that can drive critical business decisions? If you're passionate about building predictive models and using data to solve complex problems, then
our client
has the perfect opportunity for you. We’re looking for a
Data Scientist
(aka The Data Whisperer) to dive deep into data, extract meaningful insights, and create advanced models that deliver real business value. As a Data Scientist at
our client , you’ll work alongside a talented team of data engineers and product managers, using machine learning, statistical analysis, and data visualization to provide insights that inform product development, marketing strategies, and operational efficiency. Your work will help shape the future direction of the company, improving decision-making and driving innovation. Key Responsibilities: Data Exploration and Analysis:
Collect, clean, and analyze large datasets to uncover trends, patterns, and anomalies. You’ll apply advanced statistical techniques to extract actionable insights and make data-driven recommendations. Machine Learning Model Development:
Build and deploy predictive models to solve business challenges, such as customer segmentation, demand forecasting, and recommendation systems. You’ll use machine learning frameworks like TensorFlow, PyTorch, and Scikit-learn to develop models that deliver impact. Data Visualization and Reporting:
Create compelling data visualizations and dashboards using tools like Tableau, Power BI, or D3.js. You’ll translate complex data insights into easily understandable reports that drive business decisions. Collaboration with Cross-Functional Teams:
Work closely with product managers, engineers, and other stakeholders to understand business needs and deliver data-driven solutions. You’ll ensure that insights are communicated effectively and that data is integrated into product development and strategy. Experimentation and A/B Testing:
Design and conduct A/B tests and experiments to optimize product features, marketing campaigns, and user experiences. You’ll analyze the results and provide recommendations based on statistical rigor. Data Pipeline Optimization:
Collaborate with data engineers to build and optimize data pipelines, ensuring data is processed efficiently and accurately. You’ll help define data collection and transformation processes to ensure data quality. Stay Up-to-Date with Data Science Trends:
Stay current with the latest advancements in data science, machine learning, and AI. You’ll apply new methodologies and tools to improve the performance of models and bring innovation to the company’s data strategy. Requirements Required Skills: Data Science Expertise:
Strong knowledge of statistical analysis, machine learning algorithms, and data modeling techniques. You’re comfortable applying supervised and unsupervised learning, as well as deep learning techniques, to solve business problems. Programming and Data Manipulation:
Proficiency in programming such as Python, R, or SQL. You have experience working with data manipulation libraries like Pandas, NumPy, or Spark, and are comfortable querying and processing large datasets. Data Visualization:
Expertise in creating impactful data visualizations and dashboards using tools like Tableau, Power BI, Matplotlib, or Seaborn. You know how to communicate complex insights in a clear, visually compelling manner. Statistical Analysis and Testing:
Solid understanding of statistical methods, hypothesis testing, A/B testing, and experimentation. You have experience conducting tests and making data-driven decisions. Machine Learning Tools and Frameworks:
Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn. You can build, train, and evaluate models that solve specific business challenges. Educational Requirements: Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, or a related field.
Equivalent experience in data science or machine learning is also highly valued. Certifications or additional coursework in data science, AI, or machine learning are a plus. Experience Requirements: 3+ years of experience in data science,
with a proven track record of building and deploying machine learning models to solve real-world problems. Experience with large datasets, data engineering workflows, and cloud-based data services (AWS, GCP, Azure). Proven success in delivering data-driven insights that have influenced business decisions or improved product performance. Benefits Health and Wellness: Comprehensive medical, dental, and vision insurance plans with low co-pays and premiums. Paid Time Off: Competitive vacation, sick leave, and 20 paid holidays per year. Work-Life Balance: Flexible work schedules and telecommuting options. Professional Development: Opportunities for training, certification reimbursement, and career advancement programs. Wellness Programs: Access to wellness programs, including gym memberships, health screenings, and mental health resources. Life and Insurance: Life insurance and short-term/long-term coverage. Employee Assistance Program (EAP): Confidential counseling and support services for personal and professional challenges. Tuition Reimbursement: Financial assistance for continuing education and professional development. Community Engagement: Opportunities to participate in community service and volunteer activities. Recognition Programs: Employee recognition programs to celebrate achievements and milestones.
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our client
has the perfect opportunity for you. We’re looking for a
Data Scientist
(aka The Data Whisperer) to dive deep into data, extract meaningful insights, and create advanced models that deliver real business value. As a Data Scientist at
our client , you’ll work alongside a talented team of data engineers and product managers, using machine learning, statistical analysis, and data visualization to provide insights that inform product development, marketing strategies, and operational efficiency. Your work will help shape the future direction of the company, improving decision-making and driving innovation. Key Responsibilities: Data Exploration and Analysis:
Collect, clean, and analyze large datasets to uncover trends, patterns, and anomalies. You’ll apply advanced statistical techniques to extract actionable insights and make data-driven recommendations. Machine Learning Model Development:
Build and deploy predictive models to solve business challenges, such as customer segmentation, demand forecasting, and recommendation systems. You’ll use machine learning frameworks like TensorFlow, PyTorch, and Scikit-learn to develop models that deliver impact. Data Visualization and Reporting:
Create compelling data visualizations and dashboards using tools like Tableau, Power BI, or D3.js. You’ll translate complex data insights into easily understandable reports that drive business decisions. Collaboration with Cross-Functional Teams:
Work closely with product managers, engineers, and other stakeholders to understand business needs and deliver data-driven solutions. You’ll ensure that insights are communicated effectively and that data is integrated into product development and strategy. Experimentation and A/B Testing:
Design and conduct A/B tests and experiments to optimize product features, marketing campaigns, and user experiences. You’ll analyze the results and provide recommendations based on statistical rigor. Data Pipeline Optimization:
Collaborate with data engineers to build and optimize data pipelines, ensuring data is processed efficiently and accurately. You’ll help define data collection and transformation processes to ensure data quality. Stay Up-to-Date with Data Science Trends:
Stay current with the latest advancements in data science, machine learning, and AI. You’ll apply new methodologies and tools to improve the performance of models and bring innovation to the company’s data strategy. Requirements Required Skills: Data Science Expertise:
Strong knowledge of statistical analysis, machine learning algorithms, and data modeling techniques. You’re comfortable applying supervised and unsupervised learning, as well as deep learning techniques, to solve business problems. Programming and Data Manipulation:
Proficiency in programming such as Python, R, or SQL. You have experience working with data manipulation libraries like Pandas, NumPy, or Spark, and are comfortable querying and processing large datasets. Data Visualization:
Expertise in creating impactful data visualizations and dashboards using tools like Tableau, Power BI, Matplotlib, or Seaborn. You know how to communicate complex insights in a clear, visually compelling manner. Statistical Analysis and Testing:
Solid understanding of statistical methods, hypothesis testing, A/B testing, and experimentation. You have experience conducting tests and making data-driven decisions. Machine Learning Tools and Frameworks:
Hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn. You can build, train, and evaluate models that solve specific business challenges. Educational Requirements: Bachelor’s or Master’s degree in Data Science, Statistics, Computer Science, or a related field.
Equivalent experience in data science or machine learning is also highly valued. Certifications or additional coursework in data science, AI, or machine learning are a plus. Experience Requirements: 3+ years of experience in data science,
with a proven track record of building and deploying machine learning models to solve real-world problems. Experience with large datasets, data engineering workflows, and cloud-based data services (AWS, GCP, Azure). Proven success in delivering data-driven insights that have influenced business decisions or improved product performance. Benefits Health and Wellness: Comprehensive medical, dental, and vision insurance plans with low co-pays and premiums. Paid Time Off: Competitive vacation, sick leave, and 20 paid holidays per year. Work-Life Balance: Flexible work schedules and telecommuting options. Professional Development: Opportunities for training, certification reimbursement, and career advancement programs. Wellness Programs: Access to wellness programs, including gym memberships, health screenings, and mental health resources. Life and Insurance: Life insurance and short-term/long-term coverage. Employee Assistance Program (EAP): Confidential counseling and support services for personal and professional challenges. Tuition Reimbursement: Financial assistance for continuing education and professional development. Community Engagement: Opportunities to participate in community service and volunteer activities. Recognition Programs: Employee recognition programs to celebrate achievements and milestones.
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