Role Description As a Senior Data Scientist for Go-to-Market at fal, you will be the analytical backbone of our revenue organization. Embedded directly with the GTM function, you will own the metrics that tell us how our pipeline is performing, how our sales team is executing, and where the next dollar of revenue is most likely to come from. This is a high-leverage, high-visibility role. GTM leadership will look to you for the answers - on rep performance, quota attainment, pipeline health, AM coverage, and segment economics - and you'll shape both the questions we ask and the systems we build to answer them. You'll partner closely with Product Intelligence and Data Engineering as part of fal's center-of-excellence data team, while acting as the embedded specialist for everything GTM. Key responsibilities: - Own the metrics and reporting that drive sales execution at fal: pipeline health, quota attainment, conversion rates, sales cycle, AM coverage, and segment-level economics. - Partner directly with the sales leadership and rev-ops to translate strategic questions into measurement frameworks and weekly operating cadences. - Build the data foundations that make GTM analytics first-class: account-manager tracking, territory and quota models, opportunity-stage instrumentation, and rep-level performance views. - Run rigorous deep-dives - win/loss, lead source effectiveness, segmentation, expansion drivers - that change how GTM allocates time and headcount. - Shape the CRM, internal tooling, and downstream data models that everyone in GTM depends on, and set the standards for how GTM data is captured, joined, and trusted. Qualifications - 5+ years of experience in data science or analytics roles, with at least 3+ years specifically in GTM, RevOps, or sales analytics at an enterprise or B2B SaaS company. - Advanced SQL and proficiency in Python for analytics, modeling, and experimentation. - Deep familiarity with Salesforce data, pipeline mechanics, quota and territory models, and the operational rhythms of an enterprise sales org. - Strong proficiency with dbt and modern analytics stacks; comfort partnering with data engineering on production-grade pipelines. - Proven ability to operate as an embedded analytics partner - building trust with sales leadership and translating ambiguous strategic asks into clear measurement. - Demonstrated bias for action; you set up the dashboard, write the SQL, and ship the framework yourself when needed. - A track record of shipping data products that change behavior, not just dashboards that get viewed once. Requirements - Nice to have: - Experience supporting both self-serve/PLG and enterprise sales motions. - Experience working on developer-facing or API products. - Familiarity with usage-based pricing and consumption-driven sales motions. - Early-stage or fast-scaling environment experience. Benefits - Compensation: $180,000-225,000 plus equity + benefits (This range is across 2 levels Senior and Staff). - Location: San Francisco, CA (willing to consider remote for Senior and Staff levels). - Interesting and challenging work. - A lot of learning and growth opportunities. - We are currently hiring in downtown San Francisco. - We offer relocation assistance to San Francisco. - Health, dental, and vision insurance (US). - Regular team events and offsites.

Role Description Hello and thank you for your interest! We're looking for folks nationwide who are great at data entry and typing. We offer a flexible work from home remote position that allows you to stay home with the family! The pay range is flexible from $16/ph to $30/ph DOE and level of experience. You'll meet these requirements to work from home remotely: - Stable Internet connection - Work can be done using the following: Phone device, laptop or computer - Must be able to type accurately with a minimum speed of 30 words per minute - Able to focus on tasks without being distracted - Must be resident of the US - Not afraid of emailing clients as needed - Must be 16 years of age or older Data entry clerks come from all different backgrounds including, data entry, telemarketing, customer service, sales, clerical, secretary, administrative assistant, warehouse, inventory, receptionist, call center, part-time, retail fields & more. Qualifications - Must be proficient with basic PC skills - Must have an internet connection - Basic English written language - Basic English spoken language Requirements - Must be able to type accurately with a minimum speed of 30 words per minute - Able to focus on tasks without being distracted - Must be resident of the US - Not afraid of emailing clients as needed Benefits - Flexible work from home remote position - Pay range from $16 to $30 hourly depending on the role, level of experience and proven ability to work from home at the same level as from an office. Company Description Thank you for your interest!

Role Description Ford’s Electric Vehicles, Digital and Design (EVDD) team is charged with delivering the company’s vision of a fully electric transportation future. EVDD is customer-obsessed, entrepreneurial, and data-driven and is dedicated to delivering industry-leading customer experience for electric vehicle buyers and owners. You’ll join an agile team of doers pioneering our EV future by working collaboratively, staying focused on only what matters, and delivering excellence day in and day out. Join us to make positive change by helping build a better world where every person is free to move and pursue their dreams. In this role... - Architect and scale end-to-end data and AI pipelines on GCP, transforming complex telemetry and enterprise data into high-quality, analytics-ready assets using Medallion architectures. - Design and integrate Gen AI capabilities — including LLM-powered data enrichment, retrieval-augmented generation (RAG), and intelligent automation — into the data platform. - Lead the implementation of robust CI/CD workflows, rigorous data governance, and security controls while mentoring junior talent and driving engineering best practices. - Collaborate with cross-functional stakeholders and optimize cloud performance to ensure the data and AI platform remains secure, cost-effective, and highly available. What you'll do... - Design and implement end-to-end data pipelines (ETL/ELT) that ingest, process, and curate large-scale enterprise data, including telemetry/vehicle data and other structured/unstructured sources. - Build and maintain Gen AI pipelines — including embedding generation, vector store indexing, retrieval-augmented generation (RAG), and LLM orchestration. - Migrate and modernize data assets to a centralized data platform (e.g., BigQuery) using principled data lake/warehouse architectures (Bronze/Silver/Gold or Medallion architecture). - Architect scalable data models and data warehouses, optimizing for query performance, maintainability, cost efficiency, and downstream AI consumption. - Develop and operate robust orchestration pipelines using Airflow/Astronomer or Schedule Query, with secure, reproducible CI/CD workflows (Terraform + Git). - Integrate LLM APIs and AI services (e.g., Vertex AI, OpenAI, LangChain) into data workflows to automate data enrichment, classification, anomaly narratives, and natural-language interfaces. - Build and maintain reliable data and model quality checks, lineage, and monitoring with observability tools (e.g., Splunk, Looker/Grafana/Tableau/Power BI dashboards). - Implement data governance, security, and compliance controls in collaboration with security and privacy teams. - Lead the design and delivery of analytics-ready and AI-ready data assets for cross-functional teams. - Evaluate, prototype, and productionize emerging Gen AI capabilities to solve business problems and improve platform intelligence. - Mentor and coach junior engineers on data engineering, AI/ML integration patterns, prompt engineering best practices, and documentation standards. - Collaborate with data scientists, ML engineers, product managers, and business stakeholders to translate requirements into scalable data and AI solutions. - Monitor cost and capacity planning for cloud and AI resources; optimize storage, compute, and token usage across GCP services. - Participate in on-call rotations and incident response to maintain high availability of data and AI services. Qualifications - A bachelor's degree - 5+ years of experience in data engineering, data platforms, or a similar role. - 3+ years of hands-on experience with Google Cloud Platform (BigQuery, Cloud Storage, Dataflow, Dataproc; Schedule Query or equivalent scheduling/orchestration) or AWS. - 1+ years of experience working with Generative AI technologies — including LLMs, embeddings, vector databases, RAG architectures, or AI orchestration frameworks. - 1+ year experience building Semantic Data layer to serve AI agents. Requirements - Practical experience building and operating data pipelines with orchestration tools (Airflow/Astronomer; Schedule Query). - Experience with infrastructure-as-code and CI/CD (Terraform, Git, and related tooling). - Demonstrated ability to design and implement analytics-ready data assets and dashboards; familiarity with BI tools (Looker, Tableau, Power BI, Grafana). - Strong communication skills and ability to work effectively with cross-functional teams (engineering, analytics, product, security). Benefits - Immediate medical, dental, vision and prescription drug coverage - Flexible family care days, paid parental leave, new parent ramp-up programs, subsidized back-up child care and more - Family building benefits including adoption and surrogacy expense reimbursement, fertility treatments, and more - Vehicle discount program for employees and family members and management leases - Tuition assistance - Established and active employee resource groups - Paid time off for individual and team community service - A generous schedule of paid holidays, including the week between Christmas and New Year’s Day - Paid time off and the option to purchase additional vacation time.

• Design and implement end-to-end data pipelines (ETL/ELT) that ingest, process, and curate large-scale enterprise data, including telemetry/vehicle data and other structured/unstructured sources. • Build and maintain Gen AI pipelines — including embedding generation, vector store indexing, retrieval-augmented generation (RAG), and LLM orchestration — to enable intelligent search, summarization, and conversational analytics over enterprise data. • Migrate and modernize data assets to a centralized data platform (e.g., BigQuery) using principled data lake/warehouse architectures (Bronze/Silver/Gold or Medallion architecture) to power analytics, reporting, and AI/ML workloads. • Architect scalable data models and data warehouses, optimizing for query performance, maintainability, cost efficiency, and downstream AI consumption. • Develop and operate robust orchestration pipelines using Airflow/Astronomer or Schedule Query, with secure, reproducible CI/CD workflows (Terraform + Git) for both data and AI artifacts. • Integrate LLM APIs and AI services (e.g., Vertex AI, OpenAI, LangChain) into data workflows to automate data enrichment, classification, anomaly narratives, and natural-language interfaces. • Build and maintain reliable data and model quality checks, lineage, and monitoring with observability tools (e.g., Splunk, Looker/Grafana/Tableau/Power BI dashboards) to rapidly detect and resolve data and AI pipeline issues. • Implement data governance, security, and compliance controls (data lineage, access controls, PII/PHI protection, prompt injection safeguards, responsible AI guardrails) in collaboration with security and privacy teams. • Lead the design and delivery of analytics-ready and AI-ready data assets for cross-functional teams, including dashboards, alerts, self-service analytics, and AI-powered insight tools. • Evaluate, prototype, and productionize emerging Gen AI capabilities (agents, function calling, fine-tuning, multimodal models) to solve business problems and improve platform intelligence. • Mentor and coach junior engineers on data engineering, AI/ML integration patterns, prompt engineering best practices, and documentation standards. • Collaborate with data scientists, ML engineers, product managers, and business stakeholders to translate requirements into scalable data and AI solutions and timely insights. • Monitor cost and capacity planning for cloud and AI resources; optimize storage, compute, and token usage across GCP services (BigQuery, Dataflow, Dataproc, GCS, Vertex AI). • Participate in on-call rotations and incident response to maintain high availability of data and AI services.