(00:01:02) Role and responsibilities explained(00:01:39) AI impact on analytics maturity(00:02:17) Analytics maturity study findings(00:03:01) Reflections on industry progress(00:03:39) Leadership gaps and silos(00:04:27) Breaking down organizational silos(00:04:53) Accessibility in data tools(00:05:24) Data tools transforming organizations(00:05:55) Growing demand for data insights(00:06:47) Value of data science roles(00:07:04) Opportunities and challenges in analytics(00:07:56) Workflow automation in practice(00:08:15) Empowering subject matter experts(00:09:54) Automating insights delivery explained(00:11:04) Automation examples in finance(00:11:49) Workflow automation benefits highlighted(00:12:12) Leadership strategies across industries(00:13:03) Helping people realize potential(00:14:11) Overcoming self-limiting beliefs(00:15:19) Leadership advice for contributors(00:16:06) Creating internal user groups(00:17:11) Demonstrating leadership opportunities(00:18:24) Practical ways to show leadership(00:19:32) Recognizing strengths in others(00:20:27) Lessons on articulating value(00:23:02) Asking “why” to find impact --- Support this podcast: https://podcasters.spotify.com/pod/show/women-in-data/support

(00:00) Introducing Catherine Shen’s Career(00:32) Transition from Luxury to Pharma(01:55) Role of Data in Pharma(02:52) Evolution of Data Engineering(04:06) Innovative Data Solutions Impact(07:23) AI’s Role in Pharma Industry(10:24) Future AI Investments and Strategy(13:09) Solving Unstructured Data Challenges(14:02) Partnering with Math Company(16:44) Measuring Success in Partnerships(20:05) Pivotal Leadership Moments(23:22) Believing in Innovation and Confidence(25:29) Balancing Personal and Professional Life(26:12) Building Confidence Over Time(30:55) Creating Innovative Healthcare Collaborations(33:02) Confidence Grows with Tenacity(34:38) Excitement for the Future of Data(36:39) Catherine’s Closing Remarks --- Support this podcast: https://podcasters.spotify.com/pod/show/women-in-data/support

(00:00) Intro: Negative connotations in AI(00:21) Synthetic data fills gaps(00:35) Guest introduction(01:23) Importance of data quality(02:14) Data-centric machine learning focus(03:02) Bias mitigation strategies(03:41) Role of human in AI loop(04:34) Synthetic data in AI(05:29) Pre-trained models and data quality(06:02) Experiments with data quality(06:39) Leading AI and research projects(07:24) Explainability in AI models(08:57) Privacy concerns in AI analysis(10:34) Open source model benchmarking(11:33) Motivation for open source contributions(12:28) Long-term open source involvement(13:50) Mentoring in open source projects(15:19) Starting with open source(16:35) Contributing beyond code(17:50) Building community through collaboration(18:48) Power of open source accessibility(19:52) Open source challenges(20:38) Success factors for open source projects(22:58) Career-defining moments(24:49) First encounter with open source(26:28) Introduction to AI through NLP(28:02) Pivoting from PhD to industry(29:02) Career lessons and continuous learning(30:13) Advice for women in tech --- Support this podcast: https://podcasters.spotify.com/pod/show/women-in-data/support

(01:22) Research on skills and technology(01:47) Changes in job search methods(02:29) Algorithmic hiring and firm adaptations(03:25) New roles from technology(04:54) Ripple effects of technological changes(06:06) Skating to where the puck is(07:07) Building future-proof skills(08:02) AI tools in daily work(09:00) AI's impact on jobs(10:08) Mega trends: technology, climate, demographics(11:17) Testing tools and adapting workflow(12:44) AI and future of hiring(13:45) Longer time to hire with tech(15:34) AI reshaping the labor market(17:03) Gaining skills for complex roles(18:20) Turing Trap: AI vs human augmentation(19:05) Challenges for early career seekers(20:26) Mentorship and human capital development(21:41) Updating skills before job transitions(23:21) Impact of job loss on earnings(24:52) Career conversations and landscape awareness(26:31) Advice for young researchers(27:24) Staying motivated through research --- Support this podcast: https://podcasters.spotify.com/pod/show/women-in-data/support

(Intro 00:00:00) Generative AI discomfort. (00:00:36) Excited for data and MLOps. (00:01:10) First one-on-one chat. (00:02:29) Career transitions and "aha" moments. (00:03:05) Bored easily, switched roles. (00:05:22) Starting with startups. (00:07:27) Learning skills at startups. (00:09:26) Startups vs. big companies. (00:11:55) Best time to join startups. (00:13:41) Risky career, conservative money. (00:15:27) Startups in twenties ideal. (00:16:03) Label Box overview, responsibilities. (00:19:50) Importance of data quality. (00:24:05) Exciting Gen AI use cases. (00:27:56) Future of AI agents. (00:31:12) Justifying data quality investment. (00:36:54) AI concerns and excitement. (00:42:50) Building your community. --- Support this podcast: https://podcasters.spotify.com/pod/show/women-in-data/support

Join Women in Data's Datathon for free here: (https://womenindata.mn.co/spaces/10708034/about) (00:00:21) Importance of soft skills for technical professionals (00:00:39) Introduction to the guest's extensive career and leadership in the military and data fields (00:01:16) Discussion on breaking barriers in a male-dominated field (00:01:45) Leadership journey and pivotal moments in career (00:02:12) Starting a non-traditional career and early involvement with data (00:03:04) Returning to federal service after 9/11 and subsequent roles in intelligence (00:03:30) Impact of being selected as a White House leadership fellow (00:04:25) Importance of breadth and depth in skill development (00:05:19) How to lead without authority or official title (00:06:02) Developing emotional intelligence (EQ) and soft skills (00:06:27) Importance of partnerships and collaboration in the data and AI community (00:07:03) Challenges of soft skills for technical professionals (00:07:45) Practical advice for developing soft skills and teamwork (00:08:44) Peer mentoring and the value of collaborative learning (00:09:54) Adaptability in a rapidly changing tech environment (00:10:38) The need to dream big and be prepared for future roles (00:11:09) Lifelong commitment to curiosity and upskilling (00:12:00) Encouragement to apply for challenging roles (00:13:10) Starting an office from scratch as a Chief Data Officer (00:14:15) Using an agile approach to deliver capability and value (00:15:16) Building partnerships and collaborating with military executives (00:16:27) Importance of strategic communication in leadership (00:17:34) Balancing technical skills with leadership responsibilities (00:18:22) Fostering innovation and creativity within data and AI teams (00:19:34) Hosting hackathons and datathons to solve hard problems (00:21:00) Providing a safe environment for team creativity and risk-taking (00:21:51) Balancing technical knowledge with leadership roles (00:22:48) Staying current through teaching and continuous learning (00:24:13) Teaching as a way to learn and stay relevant (00:25:01) Encouraging participation in datathons and hackathons (00:25:52) Advice for emerging leaders on staying relevant and dreaming big (00:27:30) The importance of lifelong learning and curiosity (00:27:55) Plans after retirement and ongoing impact in the data field (00:30:10) Building the next generation of leaders through volunteering and advising --- Support this podcast: https://podcasters.spotify.com/pod/show/women-in-data/support

(00:00:00) Intro (00:00:22) Introduction to Snowflake and AI (00:00:34) Guest introduction and background (00:00:57) Excitement about the conversation (00:01:13) Impressions of Snowflake's office (00:01:21) Discussion on Snowflake Summit and AI/ML announcements (00:01:52) Overview of Snowflake as a unified platform (00:02:16) Evolution of Snowflake from data warehousing to AI (00:02:37) AI features and tools in Snowflake (00:02:55) Announcements at Snowflake Summit (00:03:05) Getting started with Snowflake (00:03:16) Starting with Snowflake based on specific roles (00:03:45) Specialization within Snowflake's platform (00:03:55) Deciding where to start with Snowflake (00:04:25) Importance of Python and SQL in Snowflake (00:05:35) Snowflake as a platform for various roles (00:05:57) Snowflake's learning platform and resources (00:06:05) Overview of Snowflake's developer resources (00:06:17) Introduction to Snowflake's North Star program (00:06:53) North Star courses for different workloads (00:07:13) North Star's foundation course (00:07:35) Learning paths within North Star (00:07:59) Availability of North Star courses on Coursera (00:08:34) Discussion on teaching generative AI courses (00:09:18) Differences between teaching online and live talks (00:09:37) Experiences in writing scripts for courses (00:10:30) Challenges in creating course content (00:11:29) Tips for writing course scripts (00:11:50) Experiences in teaching and content creation (00:12:12) Changes in NLP and language models over the years (00:12:24) Evolution of interest in NLP (00:13:21) Excitement about the resurgence of NLP (00:14:07) Shift in momentum for AI research (00:14:38) Concerns about the use of generative AI (00:14:59) Misuse of LLMs in outdated tasks (00:15:54) Pessimism about the overuse of LLMs (00:16:15) Exploring correct use cases for LLMs (00:16:29) Potential use cases for LLMs (00:17:04) Example of Snowflake's internal use of LLMs (00:17:25) Chatbots as a first line of defense in support (00:18:19) Content generation with LLMs (00:18:40) Example of a content engine using LLMs (00:19:23) Excitement about enterprise use of LLMs (00:19:59) Benefits of internal knowledge sharing using LLMs (00:20:03) Use cases for LLMs in organizations (00:21:04) Encouragement to stick with niche interests (00:21:50) Shift in focus from computer vision to NLP (00:22:01) Advice for PhD students on choosing specializations (00:22:35) Importance of following research trends (00:23:15) Recommendations for getting started in NLP (00:24:18) Importance of understanding fundamentals in NLP (00:24:28) Starting with core NLP papers (00:24:58) Keeping up with new research in AI (00:25:05) Strategies for staying updated on AI research (00:26:23) Following key figures in AI on social media (00:26:45) Trends in AI research and their impact (00:27:14) Challenges in staying current with AI papers (00:27:43) Use of social media for AI research (00:28:27) Academic communities on different platforms (00:29:36) Visual learning in AI education (00:29:55) Excitement about the future of AI (00:30:06) AI's impact across industries (00:30:15) Exploration of new use cases in AI (00:30:46) Examples of creative AI use cases (00:31:32) Curiosity about AI's future impact on industries (00:31:50) Potential changes in education through AI (00:33:05) Excitement about new AI-driven education tools (00:33:40) Personalized education with AI (00:33:47) The future of women in data and tech --- Support this podcast: https://podcasters.spotify.com/pod/show/women-in-data/support

Data Bytes listeners get an exclusive discount to join Women in Data. ⁠⁠⁠⁠⁠View discount here. (Intro 00:00:00) Introduction by Maggie (00:00:19) Data science roles evolving (00:00:34) Guest Introduction - Maggie Ma (00:00:56) Maggie’s diverse data science background (00:01:12) What drew Maggie to data? (00:01:35) University majors and early internships (00:02:37) Discovering geospatial data science (00:03:08) Predicting COVID-19 spread (00:03:29) Logistics and transportation data science (00:03:34) Geospatial data complexity (00:04:13) Advice for geospatial data work (00:04:43) Maggie’s journey to freelancing (00:05:55) Impact and imposter syndrome (00:06:03) Research team experiences (00:07:13) Content creation feedback (00:08:09) Overcoming imposter syndrome (00:08:36) Challenges in tech industry (00:09:57) Handling imposter syndrome daily (00:10:20) Focus on self-improvement (00:11:32) Authenticity on social media (00:12:14) Behind the scenes content (00:13:03) Sharing authentic daily lives (00:13:41) Building a social media presence (00:14:39) Staying updated in data science (00:15:50) Managing information overload (00:16:59) Career moves within data science (00:17:27) Unplanned career progression (00:18:03) Switching roles within data (00:19:10) AI’s impact on data jobs (00:19:31) Specific roles in data science (00:20:10) Lower barrier of entry (00:21:30) Using AI to debug code (00:22:00) Advice for aspiring data scientists (00:22:42) Importance of reflection (00:23:49) Making peace with your path (00:24:13) Future business and personal growth (00:24:33) Building an AI startup (00:25:42) Customized data science projects app (00:26:27) Target release date for app (00:27:01) Incorporating AI in learning (00:28:07) Encouraging the community --- Support this podcast: https://podcasters.spotify.com/pod/show/women-in-data/support

Join us for an insightful episode of the Data Bytes Podcast featuring Gaia Bellone, Chief Data Scientist at Prudential Financial. Gaia's illustrious career spans roles such as Senior Vice President at KeyBank and Executive Director at Chase, highlighting her expertise in data science and AI. In this episode, Gaia shares her career journey, from her academic beginnings at Carnegie Mellon University and Università Bocconi to leading groundbreaking projects in the financial services industry. Listen in as she discusses the evolving role of data science, key industry trends, and provides valuable advice for aspiring data scientists on balancing professional growth with personal well-being. --- Support this podcast: https://podcasters.spotify.com/pod/show/women-in-data/support

Data Bytes listeners get an exclusive discount to join Women in Data. ⁠⁠⁠⁠⁠View discount here. Catie introduction (00:00:56) Research discussion (00:01:21) Human-robot interaction (00:02:24) Choreo robotics (00:03:16) Dance background (00:03:41) Movement and emotion (00:04:29) Robot design (00:05:42) Human expectations (00:06:10) Googly eyes (00:06:53) Utility and inspiration (00:08:07) TED talk (00:08:17) Doomsday sayers (00:08:48) Robot origins (00:08:58) Robot limitations (00:10:14) Technology misconceptions (00:11:09) Robotics careers (00:13:08) Interdisciplinary robotics (00:13:28) Human interactions (00:14:25) Entrepreneurial journey (00:16:00) Zeni app (00:16:23) Personal challenges (00:17:17) Resilience and support (00:19:03) Learning and feedback (00:20:35) Future of Zeni (00:21:13) Technological adaptation (00:21:19) Agency and autonomy (00:23:21) Vision for the future (00:24:04) Role of art (00:25:54) Encouraging play (00:26:19) Inspirational message (00:27:52) Gratitude and teamwork (00:28:35) --- Support this podcast: https://podcasters.spotify.com/pod/show/women-in-data/support