Synaptics is seeking a Sr. Staff Data Scientist to establish the data science function in Synaptics with initial focus on New Product Introduction and Enterprise Sales. In this role, you will be responsible for critical functions related to machine learning, modeling, forecasting, and analysis. Your efforts will be instrumental in optimizing our Enterprise business and redefining data into actionable business insights for future growth. This role will report directly to the Chief Information and Innovation Officer and will have executive level visibility.

Millions of people experience Synaptics every day. Our technology impacts how people see, hear, touch, and engage with a wide range of IoT applications -- at home, at work, in the car or on the go.

We solve complex challenges alongside the most influential companies in the industry, using the most advanced algorithms in areas such as machine learning, biometrics, and video processing, combined with world class software and silicon development.

The typical base pay range for this position is USD $162,000 - $254,100 per year. Individual pay is determined by many factors including work location, job-related skills, experience, and relevant education or training. This position is also eligible for a discretionary annual performance bonus, equity, and other benefits. Note that compensation listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits.

Job Duties

• Evaluate financial metrics, industry trends, competitive landscape, market share, and qualitative factors to support data-driven decision-making
• Develop and establish predictive models to provide strategic insights and recommendations for market, product, and sales data and New Product Introduction data
• Gather data from various sources, including databases, APIs, and external datasets
• Explore data to identify trends, patterns, and correlations and select relevant features for additional analysis
• Develop and implement machine learning models to address specific business problems
• Create informative and visually appealing data visualizations to communicate findings to non-technical stakeholders
• Build predictive models for forecasting, classification, recommendation, or optimization.
• Assess model performance using appropriate metrics
• Partner with Sales/Product line leaders to understand business requirements and seamlessly integrate statistical models into forecast planning systems, ensuring reliable and timely delivery of forecasts and analyses to senior management teams

Competencies

• Proficiency in programming languages like SQL and Python
• Strong understanding of statistical analysis and hypothesis testing
• Knowledge of data storage and retrieval techniques, databases (SQL, NoSQL), and data warehousing
• Strong problem-solving and critical thinking skills
• Excellent communication and teamwork abilities
• Positive attitude and work ethic; well organized with strong attention to detail
• Proactive, self-starter, able to work independently in a fast-paced environment

• Bachelors degree (Master's or PhD preferred) in Data Science, Computer Science, Machine Learning, Business Analytics, Statistics, or mathematics or a related field or equivalent
• 12 years of proven experience as a data analyst or data scientist
• Solid practical experience in building AI/ML models, statistics, predictive modeling, time series forecasting, regression, classification, clustering, outlier analysis, hypothesis validation, etc.
• Experience with big data technologies (e.g., Hadoop, Spark) and cloud platforms (e.g., AWS, Azure, GCP)
• Domain expertise in the semiconductor industry an advantage
• Natural language processing (NLP) and text mining skills a plus
• No travel required

Belief in Diversity
Synaptics is an Equal Opportunity Employer committed to workforce diversity. Qualified applicants will receive consideration without regard to race, sex, sexual orientation, gender identity, national origin, color, age, religion, protected veteran or disability status, or genetic information.