• Praxisnaher Unterricht: Du gestaltest ein hybrides Trainingsformat - 2 Tage Präsenz / 3 Tage Remote direkt an der Spree in Berlin-Kreuzberg. • Wissensvermittlung: Du vermittelst alle relevanten Inhalte praxisorierentiert - Python, Datenbanken, Statistik, Machine Learning oder BI-Tools. • Teilnehmerinnen-Begleitung: Du betreust die Teilnehmenden durch regelmäßiges Feedback & unterstützt ihre Lernfortschritte fachlich ideal. • Zertifizierungen: Du bereitest unsere Teilnehmenden für die jeweiligen Module auf alle relevanten Zertifizierungen vor. • Motivation: Du schreibst Empowerment groß & schaffst so eine offen unterstützende Lernumgebung für unsere Teilnehmenden im Tech-Bereich. • Module (Einsatz je nach Expertise): Python Programmierung (Einführung bis Fortgeschrittene objektorientierte Programmierung), Vorbereitung auf die Zertifikatsprüfungen des Python Instituts (PCEP, PCAP und PCPP1), Datenanalyse mit Python (PCAD Zertifizierung), Datenbanken, SQL (Zertifikat: DP-900), KI Grundlagen für Data Science (Microsoft AI-901 Zertifizierung), Microsoft Tech Stack: MS Power BI (PI-300), Datenarchitektur & Data Platforms (DP-600 / Fabric).

• Develop scalable analyses across sales execution, pipeline health, account prioritization, market coverage, territory performance, sales productivity, and growth planning. • Create decision support tools surfacing whitespace, high-priority accounts, coverage gaps, outreach penetration, funnel performance, and execution opportunities. • Conduct TAM, segment, and coverage analyses guiding leadership investments and scaling. • Build and maintain scalable enrichment workflows cleaning, classifying, and enhancing prospect and account data using LLMs. • Develop territory and account prioritization models using sales performance, volume potential, market characteristics, business attributes, and field capacity. • Own third-party market data strategy: vendor evaluation, procurement support, relationship management, and data quality oversight. • Translate ambiguous business questions into structured approaches, insights, and recommendations for stakeholders. • Design and execute advanced territory models that optimize sales coverage, ensure rep equity, and maximize market penetration across new retail segments and geographies. • Lead the annual and mid-year territory carving processes, translating leadership’s high-level capacity targets into balanced, data-driven books of business. • Develop territory visualization and mapping tools giving sales teams visibility into market coverage, prospect density, whitespace, and prioritization opportunities. • Partner directly with sales leadership to evaluate territory health, diagnosing under-covered markets, capacity bottlenecks, and expansion opportunities. • Conduct TAM, segmentation, and propensity-to-buy analyses to guide leadership on where to deploy the next dollar of sales investment. • Migrate legacy territory planning workflows into scalable systems across Salesforce, Snowflake, and Sigma. • Build SQL/Python automations to improve data quality, automate scoring models, and streamline account-to-territory routing. • Manage territory data loads, mapping configurations, and rule exceptions in partnership with central business technology teams. • Enrich and classify prospect data using LLMs and third-party data vendors to sharpen our account prioritization models. • Serve as the Retail SME on market coverage, rules of engagement (ROE), and territory analytics. • Build automated tools monitoring ROE exceptions, partnering with central governance to improve compliance workflows and automate conflict resolutions. • Measure territory productivity, providing leadership with dashboards that track pipeline health, quota attainment by territory type, and outreach penetration.

• Work on training and evaluating models and prompts, fine-tuning, optimization of chunking and tokenization, bias detection, adversarial prompt testing, and selection of embeddings (supporting RAG); • Train and evaluate generative AI models, including LLMs and multimodal models; • Develop and optimize prompts, including adversarial testing and risk mitigation; • Perform fine-tuning and hyperparameter tuning to maximize performance; • Implement chunking and tokenization strategies for efficiency and quality; • Detect and remediate biases in data and models, ensuring ethical compliance; • Select and validate embeddings for RAG (Retrieval-Augmented Generation) applications; • Monitor quality, robustness, and safety metrics for models; • Collaborate as a subject-matter specialist with engineers and architects to securely integrate models into production pipelines.

• Own revenue predictability, planning, and executive insight across new, renewal, and expansion revenue. • Manage and coach a Revenue Insights Analyst focused on post-sales analysis; hire and scale the team as scope grows • Own pre-sales analytics and planning personally while overseeing team output and quality • Own forecast methodology and accuracy across all revenue streams • Lead forecast cadence and executive performance reviews • Build and manage capacity models (headcount, coverage, productivity) • Lead annual and quarterly planning processes; integrate pipeline signals with planning assumptions • Lead quota and territory planning processes, ensuring capacity, coverage, and quota assumptions are aligned and data-backed • Translate performance data into actionable insight, including CAC, sales effectiveness, and full-funnel conversion/velocity across new, renewal, and expansion • Proactively flag performance risks and pipeline gaps early - before quarter-end - with clear recommended actions • Deliver executive and Board-level reporting • Build and maintain pipeline reporting (coverage, conversion, progression, aging) • Support forecast process (rollups, validation, scenarios) • Track leading indicators (pipeline creation, early-stage conversion) • Produce weekly forecast packages and dashboards • Ensure data accuracy and reporting consistency • Implement approaches to apply AI to streamline forecast modeling and scenario generation • Use AI-driven anomaly detection to surface pipeline/forecast risk earlier • Leverage AI to automate draft narratives and insights, freeing time for judgment-level analysis