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<div class="page">

  <header class="masthead">
    <p class="prompt">
      <span><span class="arrow" aria-hidden="true">›</span>resume.md</span>
      <span class="prompt-tail">updated 2026.09</span>
    </p>

    <h1>Mira Kovač</h1>
    <p class="headline">Senior Machine Learning Engineer<span class="cursor" aria-hidden="true"></span></p>
    <p class="lede">Production machine learning for marketplaces, delivery logistics and fintech — ranking, retrieval, forecasting and risk. Berlin based, working across EU markets.</p>

    <ul class="contact">
      <li>
        <span class="k">Email</span>
        <a class="v" href="mailto:mira@kovac.dev">mira@kovac.dev</a>
      </li>
      <li>
        <span class="k">GitHub</span>
        <a class="v" href="https://github.com/mira-kovac-ml" rel="noopener">mira-kovac-ml</a>
      </li>
      <li>
        <span class="k">LinkedIn</span>
        <a class="v" href="https://www.linkedin.com/in/mira-kovac-ml" rel="noopener">in/mira-kovac-ml</a>
      </li>
      <li>
        <span class="k">Location</span>
        <span class="v">Berlin, Germany</span>
      </li>
    </ul>
  </header>

  <main>

    <section aria-labelledby="exp-heading">
      <div class="sec-head">
        <span class="hash" aria-hidden="true">##</span>
        <h2 id="exp-heading">Experience</h2>
        <span class="rule" aria-hidden="true"></span>
      </div>

      <ol class="timeline">
        <li class="role">
          <div class="role-year">2022<span class="to">– now</span></div>
          <div class="rail" aria-hidden="true"><span class="node"></span></div>
          <div class="role-body">
            <h3 class="role-title">Senior Machine Learning Engineer</h3>
            <p class="role-meta">
              <span class="org">Zalando SE</span><span class="dot">·</span>Berlin<span class="dot">·</span>Search &amp; Discovery
            </p>
            <ul class="bullets">
              <li>Shipped a two-stage retrieval and ranking stack for browse and search — <b>ANN two-tower candidates reranked by LightGBM</b> — delivering <b>+7.4% CTR</b> at 9k RPS with p99 inference of 38 ms.</li>
              <li>Designed the shared feature store and training orchestrator now used by <b>14 teams</b>; nightly retraining moved from a 6-hour manual pipeline to a 22-minute scheduled DAG with end-to-end lineage.</li>
              <li>Replaced offline-AUC release gates with <b>counterfactual policy evaluation</b> and online guardrails; 30+ model releases since, with no rollback traced to an offline/online metric mismatch.</li>
            </ul>
          </div>
        </li>

        <li class="role">
          <div class="role-year">2020<span class="to">– 2022</span></div>
          <div class="rail" aria-hidden="true"><span class="node"></span></div>
          <div class="role-body">
            <h3 class="role-title">Machine Learning Engineer</h3>
            <p class="role-meta">
              <span class="org">Delivery Hero SE</span><span class="dot">·</span>Berlin<span class="dot">·</span>Forecasting &amp; Data Platform
            </p>
            <ul class="bullets">
              <li>Built demand forecasting for <b>40+ markets</b> (gradient-boosted trees with temporal fusion transformers), cutting stockout-driven lost orders <b>11% year over year</b>.</li>
              <li>Owned entity resolution across menu, courier and payment data with graph-based blocking; <b>match precision 0.74 → 0.93</b>, recall 0.81 → 0.88.</li>
              <li>Introduced drift and label-lag monitoring with paging to the owning team; silent model regressions dropped from ~3 per month to under 1 in two quarters.</li>
            </ul>
          </div>
        </li>

        <li class="role">
          <div class="role-year">2017<span class="to">– 2020</span></div>
          <div class="rail" aria-hidden="true"><span class="node"></span></div>
          <div class="role-body">
            <h3 class="role-title">Machine Learning Engineer</h3>
            <p class="role-meta">
              <span class="org">N26 GmbH</span><span class="dot">·</span>Berlin<span class="dot">·</span>Risk &amp; Onboarding
            </p>
            <ul class="bullets">
              <li>Built card-transaction risk scoring on gradient-boosted trees with graph-derived device features; <b>fraud losses down 18%</b> at a flat false-positive rate.</li>
              <li>Shipped document OCR and underwriting models for onboarding, cutting manual review turnaround from <b>3 days to under 4 hours</b>.</li>
              <li>Introduced the first reproducible training pipeline (DVC + CI with artifact versioning) covering 30+ models — every release auditable and revertible.</li>
            </ul>
          </div>
        </li>
      </ol>
    </section>

    <section aria-labelledby="skills-heading">
      <div class="sec-head">
        <span class="hash" aria-hidden="true">##</span>
        <h2 id="skills-heading">Skills</h2>
        <span class="rule" aria-hidden="true"></span>
      </div>
      <ul class="chips">
        <li class="chip-row">
          <span class="chip"><span class="tick" aria-hidden="true">#</span>Python</span>
          <span class="chip"><span class="tick" aria-hidden="true">#</span>PyTorch</span>
          <span class="chip"><span class="tick" aria-hidden="true">#</span>TensorFlow</span>
          <span class="chip"><span class="tick" aria-hidden="true">#</span>scikit-learn</span>
        </li>
        <li class="chip-row">
          <span class="chip"><span class="tick" aria-hidden="true">#</span>Spark</span>
          <span class="chip"><span class="tick" aria-hidden="true">#</span>Ray</span>
          <span class="chip"><span class="tick" aria-hidden="true">#</span>LLM evaluation</span>
          <span class="chip"><span class="tick" aria-hidden="true">#</span>MLOps</span>
        </li>
      </ul>
    </section>

    <section aria-labelledby="pub-heading">
      <div class="sec-head">
        <span class="hash" aria-hidden="true">##</span>
        <h2 id="pub-heading">Publication</h2>
        <span class="rule" aria-hidden="true"></span>
      </div>
      <article class="pub">
        <p class="pub-title">Counterfactual Policy Evaluation for Learned Rankers in E-commerce</p>
        <p class="pub-meta">
          M. Kovač &amp; A. Lindqvist · <em>Proceedings of the 16th ACM Conference on Recommender Systems (RecSys '20)</em>, 71–80
        </p>
        <p class="pub-links">
          <a href="https://dl.acm.org/action/doSearch?AllField=Counterfactual+Policy+Evaluation+for+Learned+Rankers+in+E-commerce" rel="noopener">ACM DL</a>
          <span class="dot">·</span>
          <a href="https://scholar.google.com/scholar?q=%22Counterfactual+Policy+Evaluation+for+Learned+Rankers%22" rel="noopener">Scholar</a>
        </p>
      </article>
    </section>

    <section aria-labelledby="proj-heading">
      <div class="sec-head">
        <span class="hash" aria-hidden="true">##</span>
        <h2 id="proj-heading">Side Projects</h2>
        <span class="rule" aria-hidden="true"></span>
      </div>
      <ul class="projects">
        <li class="project">
          <p class="project-name">sparse-dtw</p>
          <p class="project-desc">Dynamic time warping for irregular sensor streams, written in Cython with a NumPy core.</p>
          <p class="project-meta"><a href="https://github.com/mira-kovac-ml/sparse-dtw" rel="noopener">github</a> · 1.8k stars</p>
        </li>
        <li class="project">
          <p class="project-name">tiny-eval</p>
          <p class="project-desc">A 2 kB dependency-free harness for reproducible offline model evaluation.</p>
          <p class="project-meta"><a href="https://github.com/mira-kovac-ml/tiny-eval" rel="noopener">github</a> · 2 production stacks</p>
        </li>
        <li class="project">
          <p class="project-name">Berlin Applied ML</p>
          <p class="project-desc">Monthly meetup for applied ML practitioners — talks, papers and hiring intros.</p>
          <p class="project-meta"><a href="https://berlin-applied-ml.example.com" rel="noopener">site</a> · 600 members · 40 talks</p>
        </li>
      </ul>
    </section>

  </main>

  <footer>
    <span>© 2026 Mira Kovač</span>
    <span>Berlin · 52.5200° N, 13.4050° E</span>
  </footer>

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